{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Основная идея - генерируем полный отчет за все даты указанные для каждого вагона (декартовое произведение)\n",
        "# Далее собираем информацию по вагонам и генерируем признаки - агрегаты за месяцы наблюдений. Далее производим сдвиг,\n",
        "# чтобы имея данные за наблюдаемый месяц предсказывать следующий.\n",
        "\n",
        "# В качестве алгоритма моделирования используется градиентный бустинг - Catboost,\n",
        "# Учим 2 модели на каждую задачу, метрика PR_AUC, далее настраиваем порог\n",
        "# для максимизации f1_score.\n",
        "\n",
        "\n",
        "# %% [markdown]\n",
        "# Импорт библиотек, констант, данных\n",
        "\n",
        "import warnings"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/Users/lev_grafon/DS_START_ML/env/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
            "  from .autonotebook import tqdm as notebook_tqdm\n"
          ]
        }
      ],
      "source": [
        "from functools import reduce\n",
        "\n",
        "import catboost\n",
        "import numpy as np\n",
        "import optuna\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import shap\n",
        "from sklearn.metrics import (\n",
        "    average_precision_score,\n",
        "    classification_report,\n",
        "    f1_score,\n",
        "    precision_score,\n",
        "    recall_score,\n",
        ")\n",
        "from sklearn.model_selection import StratifiedKFold, train_test_split\n",
        "from src.config import ModelingConfig\n",
        "from src.utils import optimize_df_memory"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Константы\n",
        "conf = ModelingConfig()\n",
        "\n",
        "USE_OPTUNA = False\n",
        "PATH_TRAIN_FISRT = r\"../data/raw/train_1\"\n",
        "PATH_TRAIN_SECOND = r\"../data/raw/train_2\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Настройка библиотек\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [],
      "source": [
        "pd.set_option(\"display.max_columns\", conf.pd_max_cols)\n",
        "pd.set_option(\"display.max_rows\", conf.pd_max_rows)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [],
      "source": [
        "sns.set_style(\"darkgrid\")\n",
        "sns.set({\"figure.figsize\": conf.sns_fig_size})\n",
        "\n",
        "# %% [markdown]\n",
        "# ## Загрузка данных\n",
        "\n",
        "# Cписок вагонов с остаточным пробегом на момент прогноза\n",
        "wag_prob = pd.concat(\n",
        "    map(\n",
        "        lambda x: pd.read_parquet(x + \"/wagons_probeg_ownersip.parquet\"),\n",
        "        [PATH_TRAIN_FISRT, PATH_TRAIN_SECOND],\n",
        "    )\n",
        ").reset_index(drop=True)\n",
        "\n",
        "# Параметры вагонов\n",
        "wag_param = pd.concat(\n",
        "    map(\n",
        "        lambda x: pd.read_parquet(x + \"/wag_params.parquet\"),\n",
        "        [PATH_TRAIN_FISRT, PATH_TRAIN_SECOND],\n",
        "    )\n",
        ").reset_index(drop=True)\n",
        "\n",
        "# Данные по плановым ремонтам\n",
        "pr_rem = pd.concat(\n",
        "    map(\n",
        "        lambda x: pd.read_parquet(x + \"/pr_rems.parquet\"),\n",
        "        [PATH_TRAIN_FISRT, PATH_TRAIN_SECOND],\n",
        "    )\n",
        ").reset_index(drop=True)\n",
        "\n",
        "# Текущие ремонты вагонов\n",
        "tr_rem = pd.concat(\n",
        "    map(\n",
        "        lambda x: pd.read_parquet(x + \"/tr_rems.parquet\"),\n",
        "        [PATH_TRAIN_FISRT, PATH_TRAIN_SECOND],\n",
        "    )\n",
        ").reset_index(drop=True)\n",
        "\n",
        "# Данные по дислокации\n",
        "dislok = pd.concat(\n",
        "    map(\n",
        "        lambda x: pd.read_parquet(x + \"/dislok_wagons.parquet\"),\n",
        "        [PATH_TRAIN_FISRT, PATH_TRAIN_SECOND],\n",
        "    )\n",
        ").reset_index(drop=True)\n",
        "\n",
        "# Данные грузов\n",
        "freight = pd.concat(\n",
        "    map(\n",
        "        lambda x: pd.read_parquet(x + \"/freight_info.parquet\"),\n",
        "        [PATH_TRAIN_FISRT, PATH_TRAIN_SECOND],\n",
        "    )\n",
        ").reset_index(drop=True)\n",
        "\n",
        "# Данные колеии\n",
        "kti_data = pd.concat(\n",
        "    map(\n",
        "        lambda x: pd.read_parquet(x + \"/kti_izm.parquet\"),\n",
        "        [PATH_TRAIN_FISRT, PATH_TRAIN_SECOND],\n",
        "    )\n",
        ").reset_index(drop=True)\n",
        "\n",
        "# Таргет по прогноза выбытия вагонов в плановый ремонт на месяц и на 10 дней\n",
        "target = pd.concat(\n",
        "    map(\n",
        "        lambda x: pd.read_csv(x + \"/target/y_train.csv\"),\n",
        "        [PATH_TRAIN_FISRT, PATH_TRAIN_SECOND],\n",
        "    )\n",
        ").reset_index(drop=True)\n",
        "\n",
        "# Данные для предикта\n",
        "y_to_predict = pd.read_csv(\"../data/raw/submission\" + \"/y_test.csv\")\n",
        "\n",
        "# %% [markdown]\n",
        "# ## Подготовка данных\n",
        "# 7203124 - целевое число записей\n",
        "# %%\n",
        "# ------------- ОТЧЕТНЫЕ ДАННЫЕ ------------------\n",
        "# Генерация всех дат отчетов и вагонов\n",
        "rep_dates = pd.DataFrame(data={\"repdate\": pd.date_range(\"2022-08-01\", \"2023-02-28\")})\n",
        "wagons_full = pd.DataFrame(data={\"wagnum\": wag_prob.wagnum.unique()})\n",
        "\n",
        "# Конкатенация путем кросс-джойна для полного отчета по дням\n",
        "df_core = pd.merge(rep_dates, wagons_full, how=\"cross\")\n",
        "df_core[\"month\"] = df_core[\"repdate\"].dt.month.astype(np.int64)\n",
        "\n",
        "df_core[\"month_observ\"] = df_core[\"repdate\"].dt.strftime(\"%Y-%m-01\")\n",
        "\n",
        "# Соединение с данными реального отчета для полного отчета по дням\n",
        "df_core = pd.merge(df_core, wag_prob, how=\"left\", on=[\"repdate\", \"wagnum\", \"month\"])\n",
        "df_core = df_core.sort_values(by=[\"wagnum\", \"repdate\"]).reset_index(drop=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [],
      "source": [
        "# ------------- ПЛАНОВЫЙ РЕМОНТ ------------------\n",
        "# В плановом ремонте есть ремонты вагонов по дням - дубликаты, там есть разница\n",
        "#  только в дистанции, поэтому оставляем для каждой даты ремонта вагона последнюю запись\n",
        "pr_rem = pr_rem.drop_duplicates(subset=[\"wagnum\", \"rem_month\"], keep=\"last\")\n",
        "pr_rem[\"repdate\"] = pr_rem[\"rem_month\"]\n",
        "\n",
        "# Станция отгрузки == стнация предприятия ремонта вагонов ??\n",
        "pr_rem[\"st_send_prem_eq\"] = pr_rem[\"st_id_send\"] == pr_rem[\"st_id_rem\"]\n",
        "\n",
        "pr_rem = pr_rem.drop(columns=[\"distance\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [],
      "source": [
        "# ------------- ПАРАМЕТРЫ ВАГОНА ------------------\n",
        "# У вагонов могут меняться параметры, поэтому номер дублируется.\n",
        "wag_param = wag_param.drop_duplicates(subset=\"wagnum\", keep=\"last\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [],
      "source": [
        "# ------------- ТЕКУЩИЙ РЕМОНТ ---------------------\n",
        "# Посчитаем текущие ремонты за период по вагонам\n",
        "# Присоединять в конце\n",
        "tr_rem = tr_rem.drop_duplicates()\n",
        "tr_rem = tr_rem.groupby([\"rem_month\", \"wagnum\"], as_index=False).agg(\n",
        "    curr_repair_count=(\"wagnum\", \"count\")\n",
        ")\n",
        "tr_rem[\"month_observ\"] = tr_rem[\"rem_month\"].astype(str)\n",
        "tr_rem = tr_rem.drop(\"rem_month\", axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [],
      "source": [
        "# ------------- Дислокация на дату ---------------------\n",
        "dislok_freight = pd.merge(dislok, freight, on=\"fr_id\", how=\"left\")\n",
        "\n",
        "# Есть дубликат - убираем\n",
        "dislok_freight = dislok_freight.drop_duplicates()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [],
      "source": [
        "dislok_freight_clear = (\n",
        "    dislok_freight[\n",
        "        [\n",
        "            \"plan_date\",\n",
        "            \"wagnum\",\n",
        "            \"isload\",\n",
        "            \"fr_id\",\n",
        "            \"last_fr_id\",\n",
        "            \"fr_class\",\n",
        "            \"skoroport\",\n",
        "            \"naval\",\n",
        "            \"nasip\",\n",
        "        ]\n",
        "    ]\n",
        "    .rename(columns={\"plan_date\": \"repdate\"})\n",
        "    .drop_duplicates()\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [],
      "source": [
        "#\n",
        "df_core = pd.merge(df_core, pr_rem, on=[\"wagnum\", \"repdate\", \"month\"], how=\"left\")\n",
        "df_core = pd.merge(df_core, wag_param, on=\"wagnum\", how=\"left\")\n",
        "df_core = pd.merge(df_core, dislok_freight_clear, on=[\"wagnum\", \"repdate\"], how=\"left\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Заполняем остаточный пробег либо back fill, либо forward fill\n",
        "df_core[\"ost_prob\"] = df_core.groupby([\"wagnum\"], as_index=False).ost_prob.transform(\n",
        "    \"bfill\"\n",
        ")\n",
        "df_core[\"ost_prob\"] = df_core.groupby([\"wagnum\"], as_index=False).ost_prob.transform(\n",
        "    \"ffill\"\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Заполняем остаточный пробег огромным числом, если вагон на пробеге\n",
        "# либо берем саму норму межремонтную, либо пробег оставляем.\n",
        "df_core[\"ost_prob\"] = df_core.apply(\n",
        "    lambda x: 1e6\n",
        "    if (pd.isna(x[\"ost_prob\"]) and x[\"norma_km\"] == 0)\n",
        "    else (x[\"norma_km\"] if pd.isna(x[\"ost_prob\"]) else x[\"ost_prob\"]),\n",
        "    axis=1,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Разница в пробеге - дистанция (не всегда корректно но хоть что-то)\n",
        "df_core[\"distance\"] = (\n",
        "    df_core.groupby([\"wagnum\"]).ost_prob.transform(\"diff\").fillna(0)\n",
        ").apply(lambda x: abs(x) if x < 0 else 0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Средняя скорость за сутки в км/ч\n",
        "df_core[\"avg_speed\"] = df_core[\"distance\"].apply(lambda x: x / 24)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Остаток пробега на конец месяца\n",
        "df_core[\"month_ost_prob\"] = df_core.groupby(\n",
        "    [\"wagnum\", \"month_observ\"], as_index=False\n",
        ").ost_prob.transform(\"last\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Генерация агрегатов - статистик для вагонов на каждый месяц\n",
        "\n",
        "# Скорость и расстояние (примерное)\n",
        "distance_speed = (\n",
        "    df_core[df_core.avg_speed > 0]\n",
        "    .groupby([\"wagnum\", \"month_observ\"], as_index=False)\n",
        "    .agg(\n",
        "        days_speed_non_zero=(\"repdate\", \"count\"),\n",
        "        speed_day_max=(\"avg_speed\", \"max\"),\n",
        "        speed_day_min=(\"avg_speed\", \"min\"),\n",
        "        speed_day_avg=(\"avg_speed\", \"mean\"),\n",
        "        speed_day_std=(\"avg_speed\", \"std\"),\n",
        "        distance_sum=(\"distance\", \"sum\"),\n",
        "        distance_day_min=(\"distance\", \"min\"),\n",
        "        distance_day_max=(\"distance\", \"max\"),\n",
        "        distance_day_avg=(\"distance\", \"mean\"),\n",
        "        distance_day_std=(\"distance\", \"std\"),\n",
        "    )\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Сколько дней скорость == 0\n",
        "zero_state = (\n",
        "    df_core[df_core.avg_speed == 0]\n",
        "    .groupby([\"wagnum\", \"month_observ\"], as_index=False)\n",
        "    .agg(days_zero_speed=(\"repdate\", \"count\"))\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Сколько ремонтов было за месяц у вагона\n",
        "plan_repair_count = df_core.groupby([\"wagnum\", \"month_observ\"], as_index=False).agg(\n",
        "    plan_repair_count_month=(\"rem_month\", \"count\")\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Сколько дней вагон был в нагрузке в движении и сколько уникальных ID грузов перевез\n",
        "payload = (\n",
        "    df_core[df_core.avg_speed > 0]\n",
        "    .groupby([\"wagnum\", \"month_observ\"], as_index=False)\n",
        "    .agg(\n",
        "        days_payload=(\"isload\", \"sum\"),\n",
        "        uniq_payload=(\"fr_id\", \"nunique\"),\n",
        "    )\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Остаток на конец месяца по пробегу у конкретного вагона\n",
        "month_ost_prob = df_core.groupby([\"wagnum\", \"month_observ\"], as_index=False).agg(\n",
        "    max_ost_prob=(\"month_ost_prob\", \"max\")\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Генерация данных для финального датасета\n",
        "rep_dates = pd.DataFrame(\n",
        "    data={\n",
        "        \"month_observ\": pd.date_range(\"2022-08-01\", \"2023-02-28\").strftime(\"%Y-%m-01\")\n",
        "    }\n",
        ").drop_duplicates()\n",
        "\n",
        "final = (\n",
        "    pd.merge(rep_dates, wagons_full, how=\"cross\")\n",
        "    .sort_values(by=[\"wagnum\", \"month_observ\"])\n",
        "    .reset_index(drop=True)\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Конкатенация всех статистик\n",
        "to_concat = [\n",
        "    final,\n",
        "    zero_state,\n",
        "    distance_speed,\n",
        "    month_ost_prob,\n",
        "    plan_repair_count,\n",
        "    payload,\n",
        "    tr_rem,\n",
        "]\n",
        "\n",
        "result = reduce(\n",
        "    lambda x, y: pd.merge(x, y, how=\"left\", on=[\"wagnum\", \"month_observ\"]), to_concat\n",
        ").fillna(0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Данные вагона\n",
        "wag_param_sample = wag_param[\n",
        "    [\n",
        "        \"wagnum\",\n",
        "        \"rod_id\",\n",
        "        \"gruz\",\n",
        "        \"cnsi_volumek\",\n",
        "        \"tara\",\n",
        "        \"cnsi_probeg_dr\",\n",
        "        \"cnsi_probeg_kr\",\n",
        "        \"ownertype\",\n",
        "        \"norma_km\",\n",
        "        \"date_build\",\n",
        "        \"srok_sl\",\n",
        "    ]\n",
        "]\n",
        "\n",
        "# На пробеге или нет\n",
        "wag_param_sample[\"is_probeg\"] = (wag_param_sample[\"norma_km\"] == 0).astype(int)\n",
        "# %%\n",
        "result = pd.merge(result, wag_param_sample, on=\"wagnum\", how=\"left\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {},
      "outputs": [],
      "source": [
        "# отношение остатка к норме\n",
        "result[\"ost_percent\"] = result.apply(\n",
        "    lambda x: 1 if x[\"is_probeg\"] == 1 else x[\"max_ost_prob\"] / x[\"norma_km\"], axis=1\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Сколько еще жить вагону до истечения срока службы\n",
        "result[\"last_age\"] = (\n",
        "    pd.to_datetime(result[\"srok_sl\"]) - pd.to_datetime(result[\"month_observ\"])\n",
        ").dt.days / 365"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Возраст вагона\n",
        "result[\"age\"] = (\n",
        "    pd.to_datetime(result[\"month_observ\"]) - pd.to_datetime(result[\"date_build\"])\n",
        ").dt.days / 365"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Работает ли вагон выше своего срока службы\n",
        "result[\"over_age\"] = (result[\"last_age\"] < 0).astype(int)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {},
      "outputs": [],
      "source": [
        "result = result.drop(columns=[\"srok_sl\", \"date_build\", \"norma_km\"])\n",
        "result = result.rename(columns={\"month_observ\": \"month\"})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Сдвиг таргета для предсказания\n",
        "result[\"month_to_predict\"] = (\n",
        "    pd.to_datetime(result[\"month\"]) + pd.DateOffset(months=1)\n",
        ").astype(str)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Данные для предикта\n",
        "data_predict = pd.merge(\n",
        "    y_to_predict,\n",
        "    result[result[\"month_to_predict\"] == \"2023-03-01\"],\n",
        "    how=\"left\",\n",
        "    left_on=[\"wagnum\", \"month\"],\n",
        "    right_on=[\"wagnum\", \"month_to_predict\"],\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Данные для обучения\n",
        "data_train = pd.merge(\n",
        "    target,\n",
        "    result,\n",
        "    how=\"left\",\n",
        "    left_on=[\"wagnum\", \"month\"],\n",
        "    right_on=[\"wagnum\", \"month_to_predict\"],\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Сохранение данных для переиспользования\n",
        "data_predict.to_feather(\"../data/processed/data_predict.feather\")\n",
        "data_train[~data_train.month_y.isna()].reset_index(drop=True).to_feather(\n",
        "    \"../data/processed/data_train.feather\"\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Моделирование с помощью Catboost + Optuna\n",
        "\n",
        "# задача на месяц - month_predict\n",
        "# задача на 10 дней - 10_day_predict\n",
        "TASK_TYPE = \"10_day_predict\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Memory usage of dataframe is 53.98 MB\n",
            "Memory usage after optimization is: 27.18 MB\n",
            "Decreased by 49.6%\n"
          ]
        }
      ],
      "source": [
        "data = optimize_df_memory(\n",
        "    pd.read_feather(conf.path_processed_folder + \"data_train.feather\")\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {},
      "outputs": [],
      "source": [
        "drop_features = [\n",
        "    \"wagnum\",\n",
        "    \"month_x\",\n",
        "    \"month_y\",\n",
        "    \"month_to_predict\",\n",
        "    \"target_day\",\n",
        "    \"target_month\",\n",
        "]\n",
        "\n",
        "target_feature = {\n",
        "    \"month_predict\": \"target_month\",\n",
        "    \"10_day_predict\": \"target_day\",\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {},
      "outputs": [],
      "source": [
        "X, y = (\n",
        "    data.drop(columns=drop_features),\n",
        "    data[target_feature[TASK_TYPE]],\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {},
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X, y, test_size=0.2, random_state=conf.random_seed, stratify=y\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {},
      "outputs": [],
      "source": [
        "X_val, X_test, y_val, y_test = train_test_split(\n",
        "    X_test, y_test, test_size=0.2, random_state=conf.random_seed, stratify=y_test\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {},
      "outputs": [],
      "source": [
        "def objective(trial, return_models=False):\n",
        "    trial_id = trial.number\n",
        "\n",
        "    if TASK_TYPE == \"month_predict\":\n",
        "        param_space = {\n",
        "            \"colsample_bylevel\": trial.suggest_float(\"colsample_bylevel\", 0.6, 0.8),\n",
        "            \"depth\": trial.suggest_int(\"depth\", 6, 7),\n",
        "            \"learning_rate\": trial.suggest_float(\"learning_rate\", 0.001, 0.1),\n",
        "            \"bootstrap_type\": \"Bayesian\",\n",
        "            \"bagging_temperature\": trial.suggest_float(\"bagging_temperature\", 1, 6),\n",
        "            \"l2_leaf_reg\": trial.suggest_int(\"l2_leaf_reg\", 2, 50),\n",
        "            \"auto_class_weights\": \"SqrtBalanced\",\n",
        "        }\n",
        "    else:\n",
        "        param_space = {\n",
        "            \"colsample_bylevel\": trial.suggest_float(\"colsample_bylevel\", 0.5, 0.9),\n",
        "            \"depth\": trial.suggest_int(\"depth\", 6, 7),\n",
        "            \"learning_rate\": trial.suggest_float(\"learning_rate\", 0.001, 0.1),\n",
        "            \"bootstrap_type\": \"MVS\",\n",
        "            \"l2_leaf_reg\": trial.suggest_int(\"l2_leaf_reg\", 10, 25),\n",
        "            \"auto_class_weights\": \"SqrtBalanced\",\n",
        "        }\n",
        "\n",
        "    splitter = StratifiedKFold(5, shuffle=True, random_state=conf.random_seed)\n",
        "\n",
        "    scores_valid, scores_train, models = [], [], []\n",
        "\n",
        "    for i, (train_idx, val_idx) in enumerate(splitter.split(X_train, y_train)):\n",
        "        train_x, val_x = X_train.iloc[train_idx, :], X_train.iloc[val_idx, :]\n",
        "        train_y, val_y = y_train.iloc[train_idx], y_train.iloc[val_idx]\n",
        "\n",
        "        train_pool = catboost.Pool(train_x, train_y)\n",
        "        valid_pool = catboost.Pool(val_x, val_y)\n",
        "\n",
        "        clf = catboost.CatBoostClassifier(\n",
        "            **param_space,\n",
        "            iterations=500,\n",
        "            eval_metric=\"PRAUC:use_weights=False\",\n",
        "            random_seed=conf.random_seed,\n",
        "            early_stopping_rounds=5,\n",
        "        )\n",
        "\n",
        "        pruning_callback = optuna.integration.CatBoostPruningCallback(\n",
        "            trial, \"PRAUC:use_weights=false\"\n",
        "        )\n",
        "\n",
        "        clf.fit(\n",
        "            train_pool,\n",
        "            eval_set=valid_pool,\n",
        "            verbose=0,\n",
        "            use_best_model=True,\n",
        "            callbacks=[pruning_callback] if i == 0 else None,\n",
        "        )\n",
        "\n",
        "        if i == 0:\n",
        "            pruning_callback.check_pruned()\n",
        "\n",
        "        y_train_pred = clf.predict_proba(train_pool)[:, 1]\n",
        "        y_valid_pred = clf.predict_proba(valid_pool)[:, 1]\n",
        "\n",
        "        train_score = average_precision_score(train_y, y_train_pred)\n",
        "        valid_score = average_precision_score(val_y, y_valid_pred)\n",
        "\n",
        "        print(\n",
        "            f\"Trial # {trial_id}: fold # {i}, score train: {train_score}, score valid: {valid_score}\"\n",
        "        )\n",
        "\n",
        "        scores_valid.append(valid_score)\n",
        "        scores_train.append(train_score)\n",
        "        models.append(clf)\n",
        "\n",
        "    mean_cv_score_valid, std_cv_score_valid = np.mean(scores_valid), np.std(\n",
        "        scores_valid\n",
        "    )\n",
        "\n",
        "    mean_cv_score_train, std_cv_score_train = np.mean(scores_train), np.std(\n",
        "        scores_train\n",
        "    )\n",
        "\n",
        "    final_score = mean_cv_score_valid - std_cv_score_valid\n",
        "\n",
        "    print(\n",
        "        f\"Trial # {trial_id}: \\\n",
        "        score train: {mean_cv_score_train} +/- {std_cv_score_train} \\\n",
        "        score valid: {mean_cv_score_valid} +/- {std_cv_score_valid}\"\n",
        "    )\n",
        "\n",
        "    return (final_score, models) if return_models else final_score\n",
        "\n",
        "\n",
        "if USE_OPTUNA:\n",
        "    study = optuna.create_study(\n",
        "        pruner=optuna.pruners.HyperbandPruner(min_resource=1, max_resource=500),\n",
        "        direction=\"maximize\",\n",
        "    )\n",
        "    study.optimize(objective, n_trials=500, n_jobs=-1, show_progress_bar=True)\n",
        "\n",
        "else:\n",
        "    sel_clf = catboost.CatBoostClassifier(random_seed=conf.random_seed).load_model(\n",
        "        \"../models/\" + TASK_TYPE + \"_clf_v1\"\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {},
      "outputs": [],
      "source": [
        "if USE_OPTUNA:\n",
        "    scores, models = objective(\n",
        "        optuna.trial.FixedTrial(study.best_params), return_models=True\n",
        "    )\n",
        "\n",
        "    sel_clf = models[0]\n",
        "    sel_clf.save_model(\"../models/\" + TASK_TYPE + \"_clf_v2\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {},
      "outputs": [],
      "source": [
        "scores_threshold = []\n",
        "y_pred_val_score = sel_clf.predict_proba(X_val)[:, 1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {},
      "outputs": [],
      "source": [
        "pr_auc_val = average_precision_score(y_val, y_pred_val_score)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Подбор порога\n",
        "for t in np.linspace(0, 1, 1000):\n",
        "    y_pred_val = np.int8(y_pred_val_score > t)\n",
        "\n",
        "    pr_score = precision_score(y_val, y_pred_val)\n",
        "    rec_score = recall_score(y_val, y_pred_val)\n",
        "    f_score = f1_score(y_val, y_pred_val)\n",
        "    scores_threshold.append((t, pr_score, rec_score, f_score))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: xlabel='threshold', ylabel='precision'>"
            ]
          },
          "execution_count": 46,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 3000x2000 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "data_scores = pd.DataFrame(\n",
        "    data=scores_threshold, columns=[\"threshold\", \"precision\", \"recall\", \"f1_score\"]\n",
        ")\n",
        "sns.scatterplot(data_scores, x=\"threshold\", y=\"precision\", label=\"precision\")\n",
        "sns.scatterplot(data_scores, x=\"threshold\", y=\"recall\", label=\"recall\")\n",
        "sns.scatterplot(data_scores, x=\"threshold\", y=\"f1_score\", label=\"f1_score\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>threshold</th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>f1_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>655</th>\n",
              "      <td>0.655656</td>\n",
              "      <td>0.444109</td>\n",
              "      <td>0.554717</td>\n",
              "      <td>0.493289</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     threshold  precision    recall  f1_score\n",
              "655   0.655656   0.444109  0.554717  0.493289"
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data_scores[data_scores.f1_score == data_scores.f1_score.max()]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {},
      "outputs": [],
      "source": [
        "y_pred_test_score = sel_clf.predict_proba(X_test)[:, 1]\n",
        "pr_auc_test = average_precision_score(y_test, y_pred_test_score)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.99      0.99      8011\n",
            "           1       0.44      0.50      0.47       133\n",
            "\n",
            "    accuracy                           0.98      8144\n",
            "   macro avg       0.72      0.75      0.73      8144\n",
            "weighted avg       0.98      0.98      0.98      8144\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(classification_report(y_test, y_pred_test_score > 0.655656))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.42159736190809927"
            ]
          },
          "execution_count": 50,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pr_auc_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Визуализация с помощью SHAP\n",
        "explainer = shap.TreeExplainer(sel_clf)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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8e/lVZVIM1FQxUFtNvLKC8vFxmuYGT2lTbDrPq376MPFsI7nyHBknVHgumkvOmN9g8QgRycJrd9xH/UAf0eE4lYOjDIcjZF0Hi2FyGNE8LgGS3H7mGm7ctJE9ba1sWbSAv3/1H9NdUcftSzfRE6sjXXZq9RcREREREREReT6p56CIiIjIacQeizN+/aPk9gwSeeU8xt5+Bkcu/h+i8REyrkveurSmRjBF/eTKMumZ5WA4VlbDo/XLGQ1FsQGHa/f9gEi+dF6HPBnHpaG3n+qhMVLhEA2jIzPKM9ZwxhMdVA2NE4+V0TW3irY/XXtK25j4zVEiiSyGPBEnXvLc4fJ26jODJdN6As0kAjW0pXpx8RgOx6a20xj2NdfTODrGXWtX0ltTyYruA7zrice5f8W1JeWkghH+85y3kCiroXEszpLmylOqv4iIiIiIiIg8MztxEe/v6rkp5eVF4aCIiIjIacImc4y+4vtE+8cIAzzcQeIfb2cZ44V5UoEQ9y9excaO7Zx9aAdPti9jR+t8KsfGOVTdRCifZWXfYZrG+7m3+QyyToCRhmqykRCPJ9dyfsfDhbLyxuHRuWvJB1xGq2I09A2zbNdBuquqWdDXW1K38GCW2owf5FWNJIjuTOHh4p7Shvo/DJZ5iSM87q0l7/glPV21ktrUMO2po3gYDkYX8FTFWowxHEn1cu7wU1Rm4vSX1RSKG4hFufXCtYxEKwDoqj+bzoY6TNHwpJM66lqpTyQJepbscG7G8yIiIiIiIiIiLzaFgyIiIiKniYHv7aGqf6xkWjmlw2xGchnOPbiL8myO8lSOD//mRn62/FzSVe2FeY7G6rjg4FYax8c4XNdANuIP23n/gk3k3ADL+3YzGqng18suorOmDYBEeZRswCWYy/PE3AVUJpPUxcfwgAONzQR6c1QzSJwYOYIEcx7eG/6T6Npy9uwrY3TYUt1aRfN1KynvPQaRIJmLV0No5sfZ0AXNBEnRXVVPhdPMK3vu5vGaMxkOVtMe7+WB+vNL5o9lRmkbH2Dt2E4M8IaOe9lWs5CHGtcQzObxTLgQDE7a1TSfK7Y/zB0rNhSmGWupTE31nBzuy578wREREREREREReYEoHBQRERE5TQz15qiaNq14+FDwcPCIZcewBACHWDpFMJMnHSqay3E4FmvgrGMdLB7p5Wc1G0mHQljj8OC8jdy1+AKGKmOl68l7uJ7f085JZLjlnI3E0inSwQCb9j3OuTyGgyWPwy5W0UcTXfstu2hkKFjF3O4Bgnt6qf7NTURtAoD8vAaGb/k4XmttybrcILSFd/Cvr/5LrFnPdXd8l4v7HmAkUMnuimVTdbIedZkBQrk0a+J7SoYRWT10gEPRVir6oKu+lnA6Szpceg/B1257gNbRAR6et4JIOstIWS3BiUIcL0+wTLf3FhEREREREZGXHoWDIiIiIqeJyqsWk/jSfUQzmcK0kVAF1ZkxwMMlD/hj7VuyHGAeBgPWzihrMkiLpVMs7zrCUwsWYTyP2OAYjeMpWoIBelrqGaj3h+dsHenmvO7HiXsN5EyY5NFeDrbU05CNc0Hno4XyXDyWspME5dSk8qzY2UUiN0yFHWMe+ygjUaiDe7iP6OdvJv4vf1xSt9D2Izy0YBXJcASA61/1Ltp7+omNJ3jlrofpBWLZUS7tu5tY3h9S1RLAEqX4TgNrjx3iqNfOiqPdrPyfbg601POTi9cyHo2w4fAumsZHuHznI1y+8xG6Aw18d92rGW6opCoxSjCbZcOr2xERERERERERealROCgiIiJymmiaH+HBOa1sPLAbrGEsGuYHG1/F2Z17uGD/U7hFt9AzQBVj7GQpLf2j7G2N0to/Qv1wnLzj0JQeKMzbODCE256nfCRO2XgKgFA2x5yObsYjYeKxKG996ufY0ZqJADJBBSOsHdtJOuRgcUru3xckR5t7iGy+hqp8mqXspQL/foSWCiBRmD9w+1MzttNrjGHsVK+9fMDlcFsTlz3xFPNGeumNNLB+eEshGPS3N0eCEHEaCJKmnGGMlyVYVK+Fx/p5y51bePyMdq675ycA9EcrGYjG6E82kQ44eI5hJFrBu+77OQ/9U5JfROuIeRnODKeZP6eMircuJLhkev9NEREREREREZEXjsJBERERkdOEB2w4tItqOgiToCHh8JG7OjhQu4iu6nrmD3YDkHEDPD53OUfLGwl0G87v305NPEEwNVVWmjLG3RyHWhopy42z8YGdHJxbi+e4JetccKyTi558hMbBYQ4zF4AqBqmn169QCjKUEyZeGOI0bYLc37SBBeOHWTpypBAM+hwsYczEvRLHk4bpAr/azqZDT3Dj2lcwFikHoGYszqatezkUmEtbXy9NuZ5Z9o8hRTkpyhk1VWTIwbROk3N6B3k0vJjbVlzA3voG7l+4AmscKsfHeffDv6CqI89osJxtNUtYc88hGmqHuf/M5dwUbeQN33qIshv20BrZx543X8Dyj19IrFxDj4qIiIiIiIjIC0vhoIiIiMhpIj7mUR4ZJjzuD83p4FHGAPMGw3RWNwGQdVy+fMlbOFZd7y+0DEJPZVm8r5scxffcMzwybzm/PXsFAI3Dgyza3UGG0nDwvENbeGj5eh5ucTn3yQMAVDFUMo8DDEZqqEsNkjUuj9asJW9c9lUsZNH4YchNzmkxZIA84AKW7nAtddM3NJmlKj3Mp375JW5ZvZmBSC1r9nawr7yJYNZP+7zcFtyiXoEAGaJTW2ddOipbqBnNlswTcLN88uf/yePty/jGxksK00fLy/nFmk3UpzJ0VzX6ZXge1nEI5HLkjcuWte30RWtZfqSRa771Q76VruCa68+eXnsRERERERERkeeVLlUWEREROU1UVBgax2f2mAuSoKu6ke9u+APuWHHOVDA44YEVK0qG/ZyUCfphoZPLUzaUIu2GSu5PWJGNQ9hypKadjsZmhmLlE8/MvIfhoZpW7q7axM+rLqM30EDb+ACNyWHibkVhHkMWQ37iroAGcGhKj8woK/3HF5AnSNtoN5c99TjtBwcYDMYYaCxjuCY0675JUE18WsyYCQYYiE0FhtbA3PxhLEF2N7XOKGNvQzvZbHhqfsf/qJ0LBHCB/qpmAsbFhvM80rqB5Q8+zJFj+VnrIyIiIiIiIiLyfFHPQREREZHThBN0Ga+pJjZU2nOvP1rDL888F+s4eMwcpjMdDFJtBuizU4FY1nXZvcB/PO/wMWqHRv0njAFrOWN4B6tH95BzAizsOcjB+nl0zqlkfDCAO9LM/GRnoayc4/B4wwrmbRujwSZYbDtxJwJEjwCjVBMkSRnjE0OPmol/UJZJ0vuTfWQf7yW4eR6x6Bj89gC91NFID4/UrcGaqevhUtEAmfEcWyOrGa2K0jTex4Lhw4zkmym+bi4ZCDJcFuWRtfNxrKFteJRXHXqQ2OAYWaIsGOidsZ9qR+Ks3XqAe89bTTYcnPF8KhSiMj/O+q6tzBvuoD9STVqX6omIiIiIiIjIC0zhoIiIiMhpZOBtl1Pxle+VRIAdDbWFXm4G6/f+M1NzzOntp8YOc8tZl9DePUAmGGTr0jmMxqIEMlka+odxJjoD5h2D5xgC1iPsZQl7Wd649Zf8cO5rcbIZ8uU5rr/oLZx/4Gk2HNrJeLiM21acw7xtwwC0275CMAjg4OCQo5y+wrQ0USxBIoyTxeWRf32Sdbv2U3XDjVQxAMCuxoV8d9kfU9NVdKPECaPVEe5pO7/wuDI9ytue+jGHgivxvDDjoTBHKmtJBwIcaG0kGQkRPZSgZ7SGZYOHAI8Nh/ex6cAuHly4HIBwJssFT+2jPJPmzfc/SDif41BTIw+sWk424H/kNtYyHoly19zzWRCdy8VdD5L4s2/CD99zagdTREREREREROQUKBwUERER+T1j+keJfP9+nH29ZIKVZJ7uJXCgm2QkxrbFawg5i2n2DuBMDBVaGx+eWhZwrIeHg7GWBcd6ueSJbXQzBxPJcfe5awB/YFBjLYsPdOEWjRLqehZrIOh5E/MZyvJ5lo90sLN6Pkv7d/Oanfdw2/KLuHvpOirGkpz51CGaev2eh2EyM7bHoXTozTAJjrCGOrqIZcZ43Z472d46n4VdI0CQx+Ys48sXXgvAK448TsYp7cU3VldW8ng0XMktc1/HpqMPsS+6imNV1RxpqOGpRXNIRkI0Do8yGKvlt/POYMPBnUTyKYyN8Fd33srupx/jidolxIazRLJZFo/2ELD+tjcNj1CZSHLrpg0YawuBbN516I3Wc3fbeSzas5f2vlFoqDzZwysiIiIiIiIiALOMfiQnR+GgiIiIyO8R0zNM+NX/xK9q17G98Sze8sSvmTfQzV6WEB+ppLann1GqacXDAjvdNRweW0jjsWF6W6oBcD2P1u5+aodHWHKsl2g6Q4YQDYdG6IolSZSX4XiWhYePUjM0VtLL0ACRXIZ58V6yhMlSBRjOHDzMguE+El6EpVtHaNl6F8NOFDyDF5hafpRyahkr2aYg4zO20yXHoGnhkUUrGQ+Vg81B134AfrzmNYU69TVX09I1QCIYwfXyrBrq4L7GtTPKSwUiPBldz8KxXppHxmmMxynL56gaTxLN59i5eA5vuv8xBvOtE8Obwrbm+TzWvpglB7sBqM4kCsHgpGWdndyRPoNcMIgB3FyOBT1HGSyL0Retoz3ZAbfsgPee+6yOs4iIiIiIiIjIqVI4KCIiIvJ7pOw7d3PDwtdxqH4ulck4SwaO+MEgfs80i8MADfTTTNoJcCiwGIDVTxxiqKOCsViYeH05uWiI3mgDfU11zDt4jEgyw2h5OVVjCcqTaaoHRwlk8+RdF9crDcTOGN5HyObJUU7xVXxDXgNZQgBEyNPu9dNHjHQuUriN4BHTiHEsNfk44BFmlCDJkvJzBEkSAzfHuYee4HBNGzevvoyWgRFIGsYDUQCCuTz75swj4FjW7d/DnJFjhM0YQbucrCmjPBNn0eBBUoEwibFqjrbUsmdlO69+4EmaRsboTtXQX1/JEFAZHyeYy7G7tY2Hly8m7zicvfcAuXKXgwsbqekfI1UTYs6+wZK6Otay5ug+9jbPo2FsiFc9/RhzBvsYD4b57ZwVHKppIj0aI/PxB1l0uIPh8xZz4MyVBEdTNG/roaI2SO2b5xCoCT1n54iIiIiIiIiInN4UDoqIiIj8HhnoznKofi4AnnHwMIxQNWO+fuaQcXKFxwaoHYiTqgiRC019RLSuQ09zLbGhcQ4vain0yEuWR2jr6KG3to6Wrqn7AVZlxlgw3kXOOFg7VU6KSCEYDJFgITsJkcYS4ChtHLZzWG1388OzXsmSA9uoHenGYDFYLHlyBDBYMpTTzzzAoSF3jAgZmvpGec1dT+M5YTprqmg7NsCBsjCRnD8c6b6WeexvnsNf/eYrNI30ceWj97JrbjuXHr6PgPXnGXEq+fRZH6S/tpaxaBnN/YOkykIY/CFUsfDDCzexu721sA/2tzSzoLuXgIWuBQ0MVFWysquTyuRUmBljmHc9eisAGcJkKPenZ+O8+vAdlOfHGdl3P8Z6dFU389PsIqK3bCMZCbJ/TiObfnyAZf++l+W3XUKwIXIKZ4SIiIiIiIiISCmFgyIiIiK/R5zM1D374pEoT7UvpawzSZxYyXwh8uA54JYunwnP/Hg4WlXBSCxaMnwowFhtOUNVVaQiIeYdPcpZXTtZmOgiZwI8UreSs/v9HoRA4f6GAdIs5THcwn0E07RxhEb6MLi8YcdvaEj3AExEgwYDBMgxSgMjNAOGOg5RyQB5XLpYxBz2UuX1c+YArB+t53Mtf0IuOHWvQWsc7l9wAZftfIDm9GHmHNpXsi1V3igbjj7FL2svpbovTrosWOjzOBkQWsyMfdBbXUmsp49A3iPruvzkwnM5Z9c+6sZGmD92mJpcr79fTZCsnbrXYRnduHk/nK1KxxmI1nC0uo1LOh5nZKgRk3dZerCbw3OqGTjg0f+tg7T81YoZx+a5tu+ox54jOSqihlXzXGJlhsd25cjlYG6zYcf+PBbDpjUBaquc570+IiIiIiIiIvLcUzgoLynj4+NkMhlqampe7KrM6mtf+xo33HADP/3pT2ltbX2xqyMiIlIi8MQBGr5/O9WvOIPhKr+34NbmJVx+9CESXhRvIgksI0UNI+Rtnj6vljGnAgBjLQ0Dg4zUV5SUmwiHyLou5ZlsyfSQl+O9v/k5h+qbSPaGOZaZwwCNZGyI5HiQJ6uWctbIHoI2T5g0ZYxRTW9RMDgZvGVwCeCQojKbIumEsF5FoZddiDHK6KWSPirxeyl6OCSIcpS55MkRIV4I85qy/YSzuZJwECDgeVSn+3Ap3Y5JS3sOcNfCTczv6OXw8saS5wzgmVludG79H4611A2PEgCeWLKQ5rF+nnTm0TrYR9XAGIGcZc3I4Ymy0rjkSorprmzgp2te5R+fVJLwwDhnd+5kc8cOBiprGLgnQ82cBBWLq8mtmQdFvTuPDnh09nksn+NSWW4I3P4EVe/6N8jlSVTGGP/N/8WZ1wDAwIhH37BHOu1XfOVCl6ERS1Wl4RPfSLOrM0/e+CGo8dJUGUvhNop2al/86Ndp/vIdZbTUG+5+PMPwqOWCs0IsX3B6fr2w1jKetoQC0D0Cw1lL2DUsrZ/lnJHnVMcI3HvY0hqDzQsMY2n45jYYShrqyiwXtMP8KhhNw64BuPuQpbYMNs+HW/YaOsdgVYPh3FbLz/ZC7zisqoc3LIf51TCcgj2D4AA37zUsqra8ZQXcdRgSWWipgHAAHjkKTeVw0Ry4/wjUlMHmeTOuJxAREREREXlJOD2/vctL0s6dO/nIRz7C3//937N+/foXuzoiIiIvHXkPMzyO29FHbmkbTu8w4V89gVcZJXP+cqL/cTvh256AuD+c5VWP/5xfLnsFIZPlrY/dhYOlkq0MU8Xh6hZ2NTZyJFvFOd1PcW7ycfqdWrIEGA1UcNvc9Ww4uJ21nfvJugEeWLiS29ecDRiimSzF7dyLu7uIpZLEI2HuvWw1aRNk8d5uVuzuojqRZDwY4Y76s9kw9iRzU50spo/s9K6KgJ865QGPkAdpKkgzFVBmqMQDKujFAhkiHIq0cyizEuP5NeqlnvU8QjVD9FTUkYiES9ZgrMfGQ9twpoVyxY4FWli3dQ8V8QxYW9KqbwHPMTOmL+s5TM749zhcMNjFeKSCtz75C+oSIwBsb1rM02WLSVZEiAaSLBroJUB6xrpXdO+lPD3OeLicZKSMzkVVPLp6IX9+7/d49Z774ZCBh8HigbHEKxt5YsN57A1FMcf6+J8zNjMYjbG4OssP/+kLheNUMTpGaONH+bdvfoV7n8wxHLf+JlhL2FoCTPWMTBqDDbiF7QvDVDDI1IzGWjIZ+Mx/JcgZf3LIWu57zA9dy8vgrNUhLjsvSGW5Syhg2XbEsqTZYWTE46kDecrLDeuXuRwbtjx92DK/wbBxiYPzEklSPGv5xdMeN2/zODwOrTWG16+A+TUOy+thLGXYcSzP13efx5f+doDeUUucDJ6BuDF4juOfK/j7J+hAewxeuxxGkoY1LbCgzmCsIZGwhANw/mKHf3/Y43P3WzIT+XnAWCJBw9waeO86w5I6QzIHZzbDgUHoS0B91LC+zd9vDx2xHB62XDzfUB6yDCTBNTCcMqxuhJwHu/osfQlDY7llW59hbpVlSa3BGMsHboWnuqG2DBbUWEaTML8G/vQchzVNL+yxGU1bfrAdjo1BMACPHYXtvZDLw9wq2NjuP/eLok7AxlisY6Cw/4t6t+a9iYDb347PPoC/cxzDTw9MvLZz/gl/yx749AMQNExcSjCx7QbA4aN3WXAmplnAK3pfMLYwv4Plu6+3XDDXnz3wAnW27RmHp/sMK2otT/UaftNhqA5bLmi3LKyGhiiUB5+xmBmSOX83Dab883lS3oO8hdBsb+/A0ThUhqHiFNb5fEnl/Po6s5zW6TwMpaAsAFUTf0o6RuGXBx2qI5bL5lp2DMKOQYfaCLxyrkd1GPqS8FSfw9Iaj5G0oaHM0lxeWm484++rRv/PBk/2wn1HHY6Nw5sXe5zZcPzzZCgFX9jiMJ6FzXMtF7RabjtscIzhFXM8ap5h5OlsHm45AAdGHF4517Ku0X+P2tJriGdhU4slm4cHjxnqIrCm3rJjsHQ7htOQ8aCxbGb51kIq79e/exxay8F9CXcw9yxk8hBRq5iIiMjLmuW5+Z7y0vgm+sLSxyB5ydi3bx99fX3PPKOIiMjvCedQD5Xv/jcCOzvBs9hwAEIBrOvg1cbIbVqO11BJ2Vdvw6T8YM46Brypj79TzdBTQibHnzz8fcZDURwsIeI4eDwyZwXbG5YAMAAcq7yUt+24jcbsAACPzV/BouFuNh7aBUA4l+UPdj7O4foGts5ZSG9lBbFUGmMt0XSSsw/s47F5S/jxxvML696yfiG1+Thn7TtM3QikXZfa/AAGzw+RisK5kUiMR+esYzxUzrJjnbQP9pGmAmdi3mIpahilHoNllFqOeNWFYBDAI8B+lnA2j/BU20q/oX4inAFw8h7No/2zHoeME+CfL3wPB+vaqRobp7ZnHy0dwxybWw3G+NGl6xDN51nZ1UGQJFlCnLNvJ0drGjjYWE06FMQlwxU77i0EgwCrevbRHBrE7XbIGof/7w+uZOngYd7/2E0ldbDG4JmpVtSg59d9/lBvyRE2OAyHovyvy/+U7lidv2wrjAZcco7DJ2745xn7LujleejnR0nG6ohNlOvh94Sc6g1piVhLcuI58HtDTucZcK0f5Q4HA4Xlg55HbTZH2jUczQfY9TR8/6kMaeMPC2swBDyP1nQWx8BIwCXtOGSAvDG4WFygqszwxo0uG5a6/PyJPJmsR12lQzRsKA8ZVs1xaK8zRIJTWzmetnQNWcZylmQWbNYyHLekk9BS63D2Ythy0PLoAX9w2Dn1DvPqDA8esAwnLTUVht09HsMJuGSpw5I2w9/+NM+xEUsKwBie7rX8cjdgLK611FqL3zZeB1jSQGIiZXDxw8XCscVvTD8wbPm3hycS1qf9ICvkWYLWD1c9YxjBYl23cO7mjEM8Czt6LB/5pV//wnntTAxzaz2MtZQcLmd6i7wlYPzjZgu1KvppLRMHCjAMp+HAkP/0o0fhR9s9ltT5c//xOrhqlV9+dWQinB+33HcY5lTCOe2lZ2AyC6MZqI34oYEx8HQPfPxO2NFrKQ/BJfPggS7DUMqyugFGkrCzf3Ib8Vvxi2zr8/9NZyeDOoep9wDPTo4LTMkbZ/G/ws1FS2UtU6GfLZrRLRpieGKfTaTuFL9ePQzX3Dy1mGOgOQavX2JZUQ97Bw3/s90wmoJQEJoqIBQwVIchFrZUhWB9k8fP9jl0jBqaKiwXtlt2Dhr6Ev65Fs/Cohr46w0e93QavrLF0J2YPP6l9fnSE4U9xZkNluvWexyLG2542iEPLKq0jKQNzeWWjlHoTxmaK6A3Ad1xU3KOuS5cs8zjzk6HY3F/WnUIaiKWjlGDA5QHLUnPkM751YgFYPM8jyd6DMfGDY4Dm1o9Pnd5nkcOOiyptoyk4eGj0J0wtFVARRge6zZ0jxvObrT88Zp84ZAurPIP75FR6IxDzjOUh+DsRlvSY3MwBXd3OnSOwc/2G57qN1jrz2CMX1Z5CC6b67FryGFX0Z+K8hBkPT9EKrw5mokFC+uwXDbH456jLhlv4mBb/5wpD4I1kPYM+akXII4D0SDEs6bw2vv6Tv+5BRUegwkYyUDANQRcf/3eVKd7vr1z2sk6cW69Y1meiGO576iDa6Au4v/ekygKs4HrH4fmckvANXSOT+wLpvUUdwDjFBYKuv6+AH9fGCyLaizrGy2usdxyMEA8M3nKWRzH4Br/PM16hnWNeS5p87in02EkCUfGDYmcHxp/fEOONyzyqI/Alj7DrzscfnbI4cCIIV/Y7wbjQMSFi9vyrK2zjGQMR+JwbNxhTqXHb7tdelP+56SwA21Ry4Iqj0f6/GOzsSHHxW2W2zpcnugzZDyD40JN2HLl/Dy7Bg2P9ThkrMU1hsoIbGyyzCn3eKTb0JlwqAtb3rzIozps+eLWAKNZQ2XAY0ODx/tX5TkU9//uvWauR1kAvrPH4fv7AtQELR4QduHT52RpqYAdg4ZY0NJaDr1JaIr67xNDaegcNyyvtgSnvZ13xqFr3OBZOKfREnDhyKiHyfllF9s/Ah+6N8SWAb+QsGM5o94jZAyuA5e25tg36jKShSf7DENpw6Iqy3uX5/jME0F6koZowPKBlTk+tCbPvlFDQ9jy3ntCPNbnl+kY/2KDWNAjmzcsrfL4gzkel7Z5RAKW1jIITNQr78GuYUNT1FI/EWbffNChJ2l4/bw88ayhK2FY3+Bxz1GHvSOGC1s8zm6Y5Q36GWQ9Zuy750siB7cfcfAstJdbBtKGmjCc0+jhGP/CitEsNM0SqnsTf6YCjn/xy7EktEYh7cHDPQ6pPKxv8Ggo87fp4R6H8qDl8JhDfwr+YI7H3ArLeBb+e69LzrO8b4XHUwOGB7oddg87LK+2nNecp73C0jxxYUImP/OCjp4klLlQGZp9O5M5/wKBnoRh/5jDmlqPpdVTxybn+RdJTJ7HxYqPR8cYdMQNZ9db7jrq8PMOl6aoxytaLec2ebiOX5efH3Y5ljC0lFnOqM+zfdBlZY3HhkbLPV2G3SMOb16Y5/E+h0d6HdrKJ96rHFhZ47F/1GFtvceSqmd//rzcvJDnu4j8fjHWzvKtX+RFcOutt/KpT32Kr371qy/ZnoMvp2FF+/rGXuwqiDzvjIH6+hj9/WPor5m81Jnhcco/fSPB+3aQX9CEs7ODQNfQ83J1WtoEcLAEbB5LEAjiEeA/1r6VvDPVEtA4OMryriME8MhHc/x28ZlctucRmsZGSsp7qm0+N6+7kCsefYy2gUGSoRCxTJxA1vDI3CWMlkXZuaCN3jp/KNO23gHefscDAGRxyOCQJUA1gyxkHxHijIXL+Mr572E8PNWtYvPTW1jR1UmADIGioUcBLHmqOIJDnhRRHuc8xomWzBMlzgXczW8Wb+LWNX9Q0sOvPJ3gUz//GoZx3Gk99350xqv48dpXFR5Xj4xx5a+2AA6Zcof5qQ5sLINrPRb1HWCfdyaJiXs45hzDby5YQzxWRlkqw0fv+U/KcqXl94ZqeaR+NbFMkvrsCNvrF/Om/TcT8qMnAB6cfzbfP/uNhcdHy8sYDwX5yX99jEi+dBjUXy3dyL9cdHXJtBzQEw5y9w0foS6TKHmuu6KWf77gbexuXQr47ds5M/P+icbvl8ig62Idh5DnUTbx5upYi2MtWWMIepahYIDstAAqmssxWtTz0IMZMW80n6c9nWUo4DIccP02cWsxxWVNrDODH5oGJsqajDmMgaoy/+dAAnIWelxDFkOttVN9U60lPFGH4MTyOfyG+hwU6mmBISCAxcMU8iJ34vkskCjaV8Za2u3M61P7jB90Th6PWYehhUKY5FpLaHJbHcdfdrJnqjfRGn6inpSTO6PQcl5ksruOLZq3+Ofkg+l/O4vDs8lf7LTni8oIu1AbMXTHSzrlURWGd5wBB0cdfn0QstaA48+fTnml9bJF5U4mNZ71Zy4O5jKl7wknqLSfYkzKeTN6/BbKdMzUvpptP4L/vDdRZ+P3NMQxM7fB82Y/Xt4sH1AMU2Gjtf46DP6JPhlsFupZumkznoeJ+tiZx/NkzHaFyWQdS4LRacvM9ruZZbozrX4l2zbLcZm+opL3hon/JspuKbd0j08+nAprq8OWgGPoT862XeYE653tfJ9WX29i+mRwXbyPvFmWn6x38TkDU/tlsk4l24h/Pk4GkJPrPZHJIqa3yB/vNT55Xk7fhunzT16EML3AQnhY9JxnZ9bDY1qZExc4TAbs1vpdKfG31TETfzeO98F+8j1i+olbvD8n5e3Uc5OLFL/fTJZXUj07o+hZ553tdTO5/UX7sypoGck6pdsz8XQAS85Ofy1DyNiJkNl/c2mIWBZUWrYOOiSLQ2omAqXJ15W1xIKWz56bZSBl+ME+l22DzszjV9iHE4+L38tKnpvGFM87y+vneOcalrBjec3cPL/tDvjhbdGrdvbL3Up3cFXQ+hcwxCyNZR57RhyGM4ZUbqJnfoVH1jMMpSGZM/5+wuBgWVPr8d7lOYKuHyTfe8xlIG04EjeMZ/2ArDZiCRo4u8HjsxuzVIXhwW6HL20L0J30Q7FjCQcL1IUtFUEYzvjB9/Jqj3u7Jy8MmLlTIsaSsv7xNFjaoh7rGz3eu8LjW7tdfnTA/3sVwJJj6riX7sSJkR4cP2ifvo4QlkzJiX28N/aJz3T4r7NK1yMcsIxkTdE5538edA00lVnKAv5oBnlrOJYws1y+V3ws/d+CjqWpzDIvZrm0Jc+XtgcYzjj+BQiF5Yo/AMwsr3T7Z9uOZ9rW0vkDBjY15fnW5iwVQTg4arhhZ6AQgD/SH2A8Y7lmUY6NjR7XPRBkMG2IOJb2CmiOWnoTsG/UD4Grgpas9T/frKjy+J9XZHi4z+HzTwbpThouasnTGTd0JwyvnZfnlW0eX9wWoGvckM5DImeYH/NoiVp+0+WS8yASsKyottRF4EjccGTcv3hgPOtfYNJebnnnkixf3xXkaMJQGbSsb/C4v9slmfe34zVzcuSsoWvcvzjjyLihP+lfZFEX8T/nVoYs71meozwA93Y7HB419KcNa2otV8zLccuhAKMZOK85z/5Rh4OjDpvb8iyr8vj6rgAdcUNlCC5s9vjAqhyVIcsXng7waJ9Da9QScPyw8o0L8rxm7tQb1kgGvrYjwPZBh3MaPd6zIkfkOCMO/L5qaIg980xySjzzR89JOY791nNSzsuJwsGXoNe97nVccMEFLFu2jG9/+9v09PSwaNEi/vf//t80Nzdz/fXX88ADD1BeXs4VV1zBBz7wARzHIZfL8Z3vfIfbb7+djo4OAObOncs111zDlVdeCcCNN97IZz7zGa699lquu+66wjo//OEP8/jjj/Ptb3+bJUuWPKv6PvHEE9xwww1s3boVgFWrVvH+97+fdevWFeYZHR3l//2//8ejjz7K4OAgjY2NXHbZZbzvfe8jHA4XQrdJLS0t3HrrrSddh6997Wt84xvf4Ac/+AGf/vSn2b59O7W1tVx55ZW8+93vxnXdwnzf+ta3+Md//Ec+85nPkEwm+chHPsIb3vAGhoeH+epXv8o999zD8PAwLS0tvP71r+faa68tWf6GG27gi1/8It/+9rfZunUr1dXVvO51r+O9730vgcBLpzOuwkE5HSgclJeTqquvJ3TX1hdl3VkT4Idzr2Swtq7QoNM0OMKqQ0cL81jgsRULWNe7iwUDPSXL37XsDGoH08zrm+pOkccwQgV2otXNM4afXryOQ22NLOzq4c13P0IyGGB7+zxyAZfWrkHKE2kqGOUsHuPuxedw55ILStZTNR7nD++9E4NHiPRUGyZQRUfJvQIPs4jdrCrUvpJhWs0hGtxeynIZeivq+O7Zb+BQ3VwAztv/KJv3PEokmSFAnPBEQDhUVsmfvfmTJaEpwGsefITL9z6NAWJ0EyGOBbqZw2EWl8wbLwuTTTu4nmWteZJ6O1Ty/AP1a9hTOR+AslyaCifHOYd2UU4/ATIMBmu448wNPDlnDVnHZSgSZqjMH8vuH3/xVc46urekvM9e8ofctXgd03UFA/zo+/8fy4eOzXju2rd+kuGKegCyswSD4DcGGSBjDDljSABRIJL3CiEWQNoY+kNBLBDx/Gai1MQwjpmiMeT8TjIzG14jniVVNJ9r/Qh7+nwpwBgzMXTpZA9EP/ybbC/PG7/maSBjoHhUvcmMyTVFbZrGr1fSGOIT+6BiIuibbOueXDaNf16nJrZ5UsBaWmf5ozNg/H1rJ/fxbKwtDHnpABHPFoLLuMEfjnRyPvwg0mV6L88J0xv5izkTM0yGEsdbdnrwZKY9Lq735PPTG+BLhtQsXga/5dpxpqbnvNnr684Sdk0fjzBdPCTwiRoTKQ0H80Vh5PTj5pqp8Ol44aApasCcDGtmDQGPE+jm7czqGvz1et5E+DhLqFS8/dNDqRl1pDT0OpmgcHKZ44WXs4UmJY+P8/tsJs/HGYetKGiabb2z1mOWuhxv/unzFQ7lCV6fJfWe7Tjbqedme35y3xfPV1y3yfNh+jk/PXgqDtomz4/pr6/pjnd+TJ83MFGP6XU83nkzeS5OvpFO1mVGEGlLn7PMEmra2QPurDexT8zxjy+cOLwKmtJzyVo43jUFs9Vhchtmnf+ZwofjKD5fZzs/p2dAs27fVHhaMmnSid4Opwfhs5VR3Bv9uMEgM8/B2ZxMrvMSVxe2/H8bsnzo/uN0n5Nn6WROnBdOZdBy9+tTvPJnEQbTJxM+nhxn4uSfGZ6eepnPbzmnUv7M5/yLFzwe6Z095fvcuRnetczv8f+qn4d5amDqs83lc/J8e3Pmuan2y4TCweePwsFT99JJMqTEPffcw1133cXb3vY2rLV84xvf4KMf/Sjl5eUsWrSI//W//hd33nkn//Vf/8W8efO44oor+NSnPsXtt9/OVVddxdVXX83w8DA33XQTf//3f09dXR0XXHABb37zm/n1r3/Nd7/7XV772teyePFibrrpJh566CE+/OEPP+tg8J577uGv//qvaW9v5z3veQ8AN998Mx/4wAf43Oc+x8UXXwzAxz72MXbv3s3b3vY26uvrefrpp/nmN7/JyMgIn/jEJ9i8eTP9/f3cdNNNvPvd72bVqlUnWu2srLV86EMfYtGiRfz5n/85jz32GF/96lfp6enhE5/4RGG+XC7Hpz/9ad7xjneQyWRYu3Yto6Oj/PEf/zHHjh3jzW9+M/PmzeOhhx7iS1/6Ert37+af/umfStb10Y9+lPXr13PdddexZcsWvv71r9Pd3c3f/d3fPet6P59eIrftEXneFNoida7LS5zpHXnRgkEAzwZZMDBAzXieo401JCMh2vpKAywDLO7qZkfbXOYO9uFOjC2WCIbY3jKfP9z925L5k4QLwWA8Gmbrsnk4rqEymeTMvYdIRELcetEGElE/rtm9vI2ND+6GfkhRRiow8+ZImUCQEHH8sKeMCHH6A3VU5wZwyWJxCu1TrXQwXFHGYKKVDd5vqWCi65LfJYzG+AB/+tvv8M2z38L2tkX8YvWF/GL1hZy1azfLt/ZSnknQRBcj1c6MYBBg8VA3Dh5lDOORp4cmqhimtKXTo5XD1Cb7SBHhIIvYZxdTwVNE8L/sdpU1sL9iTmGJlBti05H9OFiylJOgHicbZMREuHzrL+isbuJXK86DiU25/oKr+OxtN9A+2o8H3L14Aw/PWz2jvnn83n3dFbUzwsG8MaRCET/wcwweUGZL7sZWMNnu6wLlwKjjEMuVtq6GrMX1PKryXqGXXkXeY9yhJBycLWcKWEqCwcm6T977sLBnjV/v4kDMWAhg8CYDwqIFZmvCM4X/wBZVJgn0FAWk40BjcY/DicWC+AHh9P2Um/hX/EVqMkx0J3orOkDaFIViE43AjvH3PxM/Ew7EJoYJto5T8oct4HlEJ/MBa8kYSJ5sA/Xkuj07+x/J57JtyUz7WTy9+ASYHDvteMuXTJtlomumegKdiFsUsE4P3abLT5zxJwojihvO4TgdHSZDk6Lgo7jhf/oyhRsQOqXhJUXzFzfqm9leTUXzwvFDy2dygqJPatnJnyfq2WaZ/Q1n8rnJMmaE2M9ye+zEf5P7v7iexQH2cQOQacvN2uN0srwTlDH52iuefzJcdqfNe6J1lZR7/KdOid91aKqOJ2PyD8TxGENJ77JZA81ZQmJ47ravuBxjwLGzn5se/hjZL1RocdzzezJQLXp/mXXhIs8mgDvR34zZXgvHm3Yy6zrZeV7iBtKGv3tMTaXPnZfWSTGaNXzikeAJgkE4lTofPxQ89TKf33JOpfyZz/WlDH2p43f/+9qOAO9enuehHqckGAS47YjL4bhhfuxUP4SIyHNBf/Feovr6+vje977H4sX+lekjIyN85zvf4bLLLisEVa9+9avZvHkzDz30EOeeey633XYbf/RHf8SHP/zhQjmXXHIJV111FQ8++CAXXHABxhj+9m//lmuuuYbPfvaz/MM//ANf+MIXOPPMM3nnO9/5rOqYy+X43Oc+R0NDA9/+9repqKgA4M1vfjNXX301n/3sZzn//PMZHR3lkUce4brrruPaa68F4A1veAPWWrq6ugBYsmQJZ5xxBjfddBMbN248pWFFPc9jxYoVfP7zn8cYw9VXX80nP/lJbr75Zt7+9rezYMGCwnzveMc7eNe73lVY9t/+7d/o6Ojg+uuv55JLLgHgLW95C5/97Gf50Y9+xGtf+1ouuGCqd8MFF1xQOA5vfetb+dSnPsWtt97KH/7hHxaO2YutpiZKIHCa9dGX01Zdna7Akpe4gOPfcCV3vEvYnz9JKtjPWcTiEIsP0zYwwsG6GiLZmVdqOtk83aEa/t8r3sCqng7SboD6wRFq+uLkjCFQch81/wti3hju3LSmEAJGszl2LZ7DSGVFYRqA5zrsXt5Gw/07yeOw6tguHpy/Dlt0n70zj22llsMAZAmztWol1SMBPBzsRM8xf82WIB5nxrdh2YY5TmtlJJfh/AMP01HfyFhZOVUj46zZ0uMPj0mEJ+tXMxYOUjGeIF4+NUSp8Ty+8YrL+KeffoFjyQb2myVgDMZ6zLMHC/PNZT+t+KM1VDBGlTPI9+e8gdGOTdx1+QIyJsCand0lDYKul6cud4wKegttcKO0sGl/B1+7+DXkjaF9qJeH2xaBMYzXNnPN2z7Bkv4uonmPjBNjfnc/e9uaCz0AjbWMBhzKraWzqgHviClcwQxw56L1DEarwPMKPdPSEz34Jvcp+F/5i4fbdIBwfmZzR8JxcC0zwrSKvGXcteQmGiMNELAeuYljbKylMp9j0JnWT9AYrC0dWCzHzJ5yxR1WpjteW/1s00aLQ7iJ9eemhYPFgkBuYkjVyTLzMNWDERieaPitmdin/dN7FRkD+TyendYwbgxZ4w+zZacFBWVeab/LkLVk7NTQpSc02RB9vEBmRhHPEEycKjs5TibHD+Bmm2xnOdLTg4fjKQ7xTmbeEw49UNRSPhnenLDdbOJMLA5BZit/eq/I39WzPXTFQcWs+/8kynymMHh6eadiemB2yiHms9hB0wPC6fWBqW19pjbU2Yo5UagIU8HwZLfnE5U1vVxzEvMWd48uXu+zNT3ALtRjcjtmew1T1LvwRKHVM70uZ3HS73WzPH8yq3rGIXCfaT3PU/A763l4CqHncc/LZ12j3wvxnG7e9vtsJK+m8BdK1jrU18eIjPk3G5guGiunvv40faOR55jOo1Old8SXqPb29pKQad68eQBceumlhWllZWXU1tbS399PfX0999xzD07RPRmsteRy/vA7icTUvWfa29v50Ic+xD//8z/z/ve/H8/z+NSnPlWy7MnYtWsXPT09/Nmf/VkhGASIxWK89a1v5Utf+hI7duxg+fLlRKNRbrzxRlpbWznvvPMoKyvj//7f//vsdspJeNe73oUp+uD9jne8g1/+8pfcd999hXAQKBnyFODee+9lwYIFhWBw0nve8x5+9KMfcc8995SEg5Mh56Srr76aW2+9lfvvv/8lEw4ODSXUm0p+7xnjB4MDAxpWVF76yt9+IWXfvvsFX28fcyiOPRxrae8fIUOASNF4WxboK4/RODBK08AQFw4+RjSb4cn2xeDAgYYmlvZ2F+bPlLmQhJ6G6pIQEGCoMkZ5uvS+dwCpsjCBaIrbWy7gokMP8tYnf8o9izaRDEZYfWwnr9x7b2HeIGkWxw+zpX4Dy/v7C9HgdLNPnUoF1vTtYdGvvsTfXvGXzOkYwZl4sxiLhYjHQhhgw9a9HGxvoq+2ivFoBBxDIhzhhvVv5ez7OwuNetY4HDbzme8cYjRXTwOlPfSCXp5o1TA7ly1kf00bQxXlVA9kmFPUSzNEphAMTta/km5w57P6qD+c60gkTMDLk3MDhfXuaZjD5m17aBv056kcT9JTW4XnphkNhSBWT8YYRsNRHCx3LloHGJ5qWcwvlp8H1pZc1ewZQ8Lx21OthYAxONZijSHP1EhsIeyMZsaRgEvZLD3APGMIWUt5No81/pCZIWtJGkN+otygZzET65nabx4VnkfKcQrDcnqzNW5ONOJOTvUKE31pZg4rOtv5MdufiwQQnjYtC2D9+/w0AHnr3/MozFQn1cDEz/TEtk+ub9ZhRY0za4O3mWV+w+xt3S5Fo+QdL3hwp4Wfx2vUnu0+ZoXZjrP/p5dXPIzgbDt8Ymj+qTDgJP9Ye/5ZUHI/wKx3nF4ws5Q5Ga48k5MKISbqXRiyk+Mk0ccpa/pmB93S4+GYZ+4RWTz8Zsn+L1r3ydSl+Hkove/c9NmLh7V8LpX0zKP09+nrs9Nmejaf9Z5xHzD7FQfT6zdjqFFK99/xemYdr/cVxg/Nj/uaLHpdnqj86SbvgTiZx5fsWzv1/GQgnp/lfWG23RUwU6Hj9PsHnqj3nYcf5h/vOcdO24+cROA68eTkm+PkfjreG+azYme/oGDWt5yTDAqfqRdq0apLPhRMr8NsQ4Oe8LVjSn6ckJmlwGfxVv37yDWWqxfl+ebuF6K59BSCXPkdWf7XqgyPd4dIz3KfSP++iKd2TKqCnn+P0eOs97k51s/vOXPi7Z+57mjAsqrG49G+2S+xu3phlv7+JGuiMKc8zJHxqf2zrt6jkST9/bMu+nupvl4XtctLj8LBl6ja2tqSx5P3vJs+3XEcvIn7W4RCIX7xi1/w4IMP0tHRQWdnJ+Pj44AfFBa7+uqr+eUvf8mOHTv4sz/7M9rb2591HY8e9e9TNBlcFps/fz4Ax44d44wzzuDjH/84//AP/8BHP/pRQqEQ69atY/Pmzbz2ta8lHJ7eFHPqigNAgDlz5hTqUWz6fjx69CibNm2aUV59fT2xWIzu7u6S6ZPbN2ly/03uk5cKhSVyurBW57u89MU/+0fkzphP8N7t5Bc0EfnGr3HGUs/b17vJl0SSihnPOVjShEmQp4IkQ5EK+isqGI+EsUCqrIzHm1exfOgg5x3cTmV9irsWbaC7upr2wQH6K2NcdOx+PKL0eTPLB0jNctX18u4DbEg8SepAGcZaVnfvYHX3jon6OjN6AEbzCSrHRhmkjmYOTduykxfNpblg/2P05OZP1S8y9SU2lM2x7GAXc47189sNU0ObjwQqCw1yedfQ1xIlWRGkM1/Fsv5OzMDMumRdl+0r21h3oJOHl8zj/tVLWX2okxVHjhFLp2nJDM7S4cZypL6u8LgqlWZFdx9b21oK02oSSdoGhwuPmweHWd+xnSs67uDvLnsfj8TqyRvD4kF/VIZNh7fx7bNfw7bmRazsOchv25dRmS2+ZxswEdhZY/CsJYQ/1GVxz7S8MaSKehlOtp0mHUP5tGAp4/i9S8OA41kCE2FZ1FpGjaE/GMRgiebypFwXg98briqfJ2cMSWMKHbMcj1l6DdmpjjF2qnMW1lIWhouXGh46AGPJ4n3r84Cs8YO9mLWkpjXSpowhaS2hiWXSQBy/12AYGMIfurSMqfboyTMoPpEXlZ9E22oQS7boDDDWYuxEGFq0vZap3onFCkfQULh3YUlDzYx7gDHV8D490Cuu4IxeZLM0FhfCQUr/6J6okXwy3DGG1pgl4EHHyHHmM9Me5zzIzbKeEzVcWyCTp6RnXLFT6ZE0fdnin1B0Mh4vhOX49zcrlM204zG57ESZk+FH4bhN24bikORk9lNx8DdbIPJMQUshlJxWxvHWOVmvZ7oX4mS9TqK3oDF+bpWdEXDPUonZwhXHTAw7a6fC62c6NwrbwdRy+YngbbbhZCd3UPF9Pyfvg+eeIJwrfrOd3KZnCrvz1m/dmdyu4je/Qj2ZCh9nOx1Lev3h38OvJMhm5gUFk+f3ySjev97EwhPHwjWQzxVNmyx7+sqcon3qFs83y/6Z7QKIScXHEqZeZ5NBvZllXormLT4eJzq/7bQH0y/csBYz0Xt+6rmZ5VQF8ziOYShT+mYcnO01QNFxnL6+yfrPqJ+ZPXOYddrx3uumT589xIi4lpw3dVoeJ4EtTDdYqkOWRVWWVA62D/mjSbjG4k7s9qALQQcqApZEzjCW9cueDDkc/AukIq5lQczSWm7JevD0gMN4zr/+xMNQE7L86/kZLm3ziGfhloMuWWsoD1giriWVN4Rdf719qdIT38USci3JfPExorANtugRWJrKLNcuyXH900FKT3pD6b7zlwwYvzNxaaB1sgdtNpawU1zezHX6FySVluVgcQzk7NTxmb5tZ9fnmReDhgjc323YPuQcp06WjY05nuwPkPZM4XvA1FvgyWzHibex+G0r7MKnzs5ySavHDy/L8C9bA/QkDRc0ezTEAgyPZ7lyfp5/fTrAL4+4hT0yOexl57gh70FFEBrKPA6MTo3T8ar2PJ87N8uPDgToGjfcf8ywd9Q/R0IO/PO5GbYNu+wdht3DDoNpQ0OZZSTNRKDolxQLwqoaj6XVHvGs4WjcsH3YkMgZ6iJw7ZIsPzsc4MCYIetBVQAyFlJFg9PMrbA0RPzzfOugw6Exf89WBf3XaMDAH7Tn6Es5bOl3qAlb5pR7bGr2j9239wQYyfjn/V3HXJI5qAzCdWuydMQdHu51iGdhWbXlI2fkWF7t8W/bAjzS61AZsgymDOM5wxsX5PnQqpz/GnXgxj/I8NknA2wbdDin0eNjZ2XVhiPyEqBw8CVqMgw8Wel0mve9733s3r2bs88+m3POOYd3vOMdrFu3jiuuuGLG/AMDA3R0+MNg3XPPPVx77bXPuufg9MBxtueCQX/YqMsvv5xNmzZx9913c//99/PII4/w0EMPceONN/LNb36TUOi5udlzIFB6Sk8Gp9O3bfrjE22L53kzyjXH+XL/bI+biIicRlyH1DsvJfXOSwFIfOItuDs6iH7ifwhuOYCTzEy15cXKyC1uxj3chzMYL2lvPdmvyQbwcAgA0wcQTU98BBynjDKSHK6rKQzvOVIXIxUNM0wle5sWsLp7LxsO76F27EEsBuOmWTPWRzbokguGOaP/AE+PzKevqrpQ/rLuvfzRY9/nJ8nX8uiCs8g7Ls2Dfbxhx510VTdyqLaFdCbAJUfvJexl8HBIUwu4OGQIMoZDHkOOxendHGU+R5lPC4eecftzBAiQmzF9Qd8xjrQvwpgs8+wBKrIVbGVlyTzjZaUXLJlgnsnGkv7mKMmY/3klEwiytXkB+fIMF3U8MrV8KMJdizdQkR7njdvv4X0PD/HknPlUD7k0jsUBcMj6+7GoKSXtBqkcj0NsqmFtzbFu/njLj/nVsnPorG7FWJdEOEh5Ko01DtXpYS7ofoiBaCVPti3DA+Kuy2jYD2uzxuGy3Q8SyCT44epLOKtzN9taFhMs6rVn8cOmqrxXuBfhbJ+IMo5TuAelYy1VuTzHwiECFirzfrNRyhiyxhCZ+EzlGUMCP0S0GMZdl4jnEct7JI3BMfCm9Q4/e9KjLxvAGkNZyBIJgs2DgyFWaRhKwlgKqiKwrNFw6SqXaNjQO2zJ5MBxLZeuDNBSM/XZLudZfrsnT98YxCKQycGqdkPQdRhNeHz1zhxbBz1GMDiuoSoAc2PQlzSEXMOliyyP7Lcc7Ie+NIxM1DcDjHl+T8IIfqPZOH7DmZm242J5j9HiHnzWgudRBgTynn/vwIkwdGWdpTPl0J0sLSTpGKKeLWRCaTMx1OpkIJjPz2ygLb7HoGWqAbsQEDIzUJzRmAuFNLCkeMuKeljTaFjZCFethP/ZCoksvH0N3HfYsLUX3rAczp9r2NEHT3RbGitgXbOlrswv9+CQpWsUasvg8w/C0VEYTlsOjVDUo9RSFoRrVlqe7DEMp+HgEKVhWXGVp79BHveN4jih0azfBaalAzPWeZwwAmaGLEX7uIw8aVy8ifcB13gEwoYz6i1hx3Js3J83nfdDg0TWYzDt4DjQGPVoLoeMNcyP+Y2TTeWQzsPNex3GM5DKW0az0yo7o6eh/8MxlkvnWjrHDY6F1Q2Wq5Z6fHObw286/IZHB5hXBR84y+Ntyy1/d7/h60+XNvQaYzm/1dKXNuweNDPfSCbPO2uZX+Wf0z1JQ2UEWqKW4bTDaMojaw05DxZWe/zDBR6/PWr45jaHbB4uafd45XyPB465rKmzvH2Fh2fhpn2GoZTh2pUeVWHYMwTVYagMwT2dhns7HW7vMBweA+zEBQiFbMf6re1TKQXhIGQtGOsHBpNW1nn8x2V5PvmAy11HJt5vJm4bGTCQm7x/5fQTpfj8nDwnDH5AONmDcDJcMlAWoLAfSpaZLZibvo+BmpDl3MY8vQlD17hhMOU34odcCtcUZDzI5qcHEMVlGRZVeVyxIM9XtgfI5ifmM6b0Hn4WAvhBj1/MRFhgLQHHEjYwXrilpv/cugaP8Zy/ORe2eqyosSyqtpzbbPnPHS5f3eYSzxrW1nk0lnncesglmfcvZDGOIexCzPUYyPqN8o1llqCxHBt3iDoereUevUnDUNoh5FiqyywDSUPe+sNtx0L+sNUJb6INwBbV3VpirmUsB8urLRe0etSXWbYPOewcMsSzhsVVlk1NHiMZyHmG3xyBjriLtZbFVf6OGcvAmlqPM+osS6stK+ssj/Y65DxI5ixd4w7DGcPKao+rFuWpi8DuYcORuP/J4JJWj4gL+0cNFQGL4/ghizEwnIbrnwrQkzD80bI8F7T4+zMeqKDWi3Ng1DCUNiyIefQl4X89EGL7kOOfyxPnoWPsRBDmsSDm0VoOC2IewxnDI70OIxlDJu+HFIsr8+wfc+iIO7iO34CY9KY+lfq/+et7++IcP9jvhy3nNOT5izNy1IbhyLihNwkLKv2dvb7BK2S8x8bhV0dcdo8YuhOG9qjlTYvynFVv2T3sb8v6Bs+/ReuEdN7fx/VlJ3g9TBhIQTTgZ8kHRg1zKyzlwdnnnd5R9ysXZvnKhdnjduB9rNfw33sDpPOwqcnjDQvyVE40az01YHi636EybFlcaVlV6++vkTTcd8xhTZ1l3kTg9Odr8tx8yKUu7PGKdsvhMUM8a9k36rKw0uPMutI31KcGDP1JmFsB1WHLN3e7PDXg0By1rK/3eNVcj3uPOjzS61AThjkVlp6kH4QurvTff9rKLW9ckKciCPtHDONZOKPesn/E0JOc2ueOge2Dhif7DJ0JhzkVltfP91Oou4861Ib9bZ/8E9ObhLrwzJGzexLw606XgTQ82e/QmzAsrfH4v2fnqAoD5OhL+u/dwYll41n46SGX33Q6eMDSKo91DZZ5FZZY0HLnUZcnBxxWVvvr700aqkN24j0PogHDHy7N0Ryd/fhuavbY1Ox/QzIG6usj9PcnsRa+uTkLzDz2s+XiYxl/WsXEefXna6a+g8Sz/t+RqomvF1cf5028LwnVIT/kPhkfPSt/3PrMdr4mcv51U9Un2S/jotZs0aPp+2H2W2R8fN3M717TLai0fPWi7DPOJyIvLIWDvyfuuOMOduzYwSc/+UmuvPLKwvS+vr5Z5/+nf/onstksH/zgB/nKV77Cd7/7Xf7wD//wWa2ztbUVgEOHDs147vDhwwA0NTWRSCTYs2cPCxcu5Morr+TKK68km83yxS9+ke9973s89NBDXHTRRc9q3cfT1dXFwoULC48nA9DJHoTH09LSUqhzsf7+fsbHx2lubi6ZfvToURYtWlR4PLlsW1vbKdddREROP/mVcxm76W/8B/EUJp3FVpZBcOojmukfxekZJr+8HRNPUvblXxK8d7v/XC6PSWQgl8eMJXEHxkrK93AIkccjRYowHg4pgqTxv8G65IkTozExQk95DdmgSypa+s1xNFBJ1isnmp6KGH+55pX8ZvUZ/NltP6NtaIirHryPLQuX0F9ZwdLBXZx38GEcLFdt/xlnHd7Knc0XUZ8e5p4lZ3PHinNJBQLkHIfbExt53RO/5qyjnQQnvi/nCeARIkIvLjkqGaScEYZoZ4g28njU0E2g8OXUbzQZKKtiT90yMmHDxfsfJk4lBks5Y1hgRW8n98/1WOs+QFVujJbREEfKWxkOVfulGAg6EwNVWsuVjz7Mqs5Ofr3+LHqragjkMzT0DxIoai2+c+EmdrbP58zOHYyUVfDzlRfRX1HDRUe2Ex0LkaCJpR1J+sNRso5L0MuTMQGOhZtpTnXjYMk6AXqD81nd1cmqgR388/nXMhYuI5RP0JRqZfP+LcwZ+iX18SE6qxv57zNeySUHt3FB55NkNyzme6++mkC8jPxgnupsjm+tv4I37/otldkUlcMpYrsf4g+fupMr3vMFKizU1Rr2j4LrQdBaYp4lMxGOJFyH8rxl+ihLfrupmTinoMzzaE5nGA249LkOjudRgV/eZPtxMGxIOC6ua7hgsaG/xyOestTVB1g01+WVa1zaax0+cLlla4dHTblhQeNzc4+fgGO4ePlxvubUuvzbO0+i9eVi/0fPmMc3H7c0VFjSOb9hryIEP9lqyeQs89K9hMJN2JzHoSSkMn5PwzJryebzJCcvSPMsQfweia9cCn99iUPvuGFDm6E85O/bzhHLh3+a47Gjfk+QvDGkg4DnhxV2okfUuXMMHzzXYVGN5Z5DsL7NUFNmyOWhqgx+uNXSM24pcy2do9BSafjTDbC7zxCLGOZWWrb2wpI6SOXgU/fAgUF4xUIYSMBdB2A0MxGUBKA+CpfMh78+31AVKT1Gf1E08MaCmtJduLLB/zfdghpTmPe/rpz5/HDSDzPqo6XTf7ITPn2/oWt0cthZiLjQUAahAOwfmspgXFMUdpe0q5rSIRcLYdn0gHC2F4EvFrKMZWZ9kVAesoxnTKEH0fwqy5+cDWc1W7b1wRWL/VDUszmOjkFzBSWN3qWVKnaiVMj3N+dONdhl83DrfsOOfsPGVktV2NIdh65xQ3UYDg7DmgZ4zUI76+0PL513/PV9+mLLP16U5/EeeLrXcGG7ZUnRoCyTI1b2JWA0A8smnhtM+Y2+sWnXhPqNslH6+2cOEb+p1fJX60vr8pZlpQ2T71hRmr4tLToPXzXf8qr5ef5x4nHO8/d3Kgcjmcl6GRoilm2Dho1Nfig9aSAJT/QZVtVaWiY6yf/gtXkePubx9e0O4znDH8z1uGapRyQAT/QYbjngML/S45VzLRUhP2DpGIPH+xwWVlrGsoY5Mb+B2zF+wPBUn6Exann1fEvQ8Rtxv7XLYd+Iw7uX55lXadk34odbH/1tkJ5xw5ULcmQ8vxfLNcs9Wiv80LWmdJTv47qvy+/NUxu2tJVbGsssX3jCZThjeMuSPG9Y5O/TT5wztb+f7vcDrCXVHrcfdlhaY/mDuf58j/YYtg0YLmqzEyHQVGNy3oPuBDRFZzvfp7xnZZ73rCw9vv968ewN0ZPXPRxv1NWsNxUyJHMwloXG44RJ3eOwb9SwodESdqfOkyknuF/0uTAxCPUJLa8uLmPm62tV7VSANGlx1cz3guow/MM5pY3wFUGYX2/o7/d78ky+Hpqi8OvX+Sd6T8JfNjzx5694/zwb1kJPEmrDEJqlrOvOmLmvFleXpOMlz7WUw7uWz75/i7elWNiF8EkEgwB1Ra+HFTUn7qJ0vHPpeNPXN1rWN85+7M+ss5xZN3O7qsJwxfzS4x8JwDWLp+adfP2smWX5ybKL/fXa4sHgfVcu8LhywfTzbPbyFhWdZ4uqbMljKD43S8u7Ypa/E8d7jTVF4R1LJ9c/ez0api1bEYS3L8nz9iWzz3/t0jzXnui1Oc3JjtB8omVm6yMw/W9asYrjBNHTTd/2kzVbfWbbzmjA/3eqTmXficjLh8LB3xMjIyMAJcEYwPe+9z0A8vmpP5q33XYb9957Lx/+8Id517vexWOPPca///u/c/HFFz9jiFZsxYoV1NfXc+ONN3LVVVcV7jsYj8f50Y9+RH19PStWrGD79u28973v5brrrivcqy8YDLJs2TJgqhff5M8T9eJ7Jj/4wQ/4m7/5m8Lj//7v/8Z1XS6++OITLnfRRRfxne98h7vvvptLLrmkMP1b3/oWQMn9BgFuvvlm/vIv/7JkPcaYZ1yPiIjIcVVEsBUzW/VsfSX5+kr/96pyEh+/Cj5+1Yz56hb+yYxpLjmC+EOXlpFhgBgpAjDRY8XviOBQNuTRlB2kv6JmRhnzB2ZeaLSq8wg/X3sWT7fPpWVomLJslvN376CcbsopvXHE/PgR2vpG8dwgB+taSQYCZCd65I9UxPj+ptex4qf/RTCdmtrmiYDQJTMxpFCUGoYKz28vP4vO+ipqcoMYz2HQqeexxauoyw3QX1ZNbH+QBP4+q2SA5TxNyPNoH+2hzPPHnAx7Ga7s+gVdZa10VbTw6Jx1hPIe77vr59TFR1kwcoTPv+pdjEQn7w1RRjocYt7hLkKZHIG8pWIsyZZFq3lo/pmk3AAYKM9k6a2up7hHRk0mya1rzyLgQiiZJOuuJZTPsGRoH+uO7iSUCpIPZmgd72Wo3LC92R9ONECCd205RH9FNY+eeS7r4gf5aNcduGfPYeymz+G113EVcBWw9YjHt+7M0DtUw62vuZIrfnkLxkJdOs7ID/6SL6wpJ521zGlySWU8bnk4TyZjKbN5yoKGNcuCfPi/MiQSeULThkx0PVsYNCpvDImQIWwM7WWWNUtdQq5Lc5VDfbXDWUsDVJSdfCtC0DWsW/DSHXmhKebw0UtmTv/wJsjlctx220O8+93vprt7CNcNcGzUEglYhpPQMQRntRsqI4bxjGXvACysgcqIv39WTiuzvcpw87V+S5K1lnQeIoGpfdk1aqktg7Lg1LQl9TPr9sGNs+//8+ZN/ma4YN7U9G/MEtC9mKqP00j2phX+P9/MbUxkLXsm93EY4hn47jb48S7/dP6DBdAWg//Z5vd23NAC582Bx7otj3UbOkYcsh7Mr7S8doklnvFDjU1t8N3tfk+0D2+AhdXQNWbZPwT/vR2GUoaV9fChs/0w6Na9ltE0vHaxv75J64quNXQMtFc+V3tspqALb1pqedPS6d+rTv17VjFjYH0zrG+eWd7kcJ8tFdBSNL32JEOr59Nk4BMJ+P8AmqL+NlwSnbktdWXwyrkzp29ssWxsmdkofVaT5aymmdMXVMGCqqLudkU2NFk2NJVOC7nwvlUexY3xayYCgZ+9/rnpdXFhm+XCttK6fnnziRvaz6i3nFHv12NpdWkwMNt2THIdaJt9BPJT9kyN1cXBV1nA/3c8zeXQXDQu9IkCzJerpmkXW5xKMAj+67v5OSpLREREXnwKB39PbNy4Edd1+T//5//w1re+lUAgwH333ceDDz5IMBgs3HtwaGiI66+/noULFxZ6Cn70ox/lmmuu4e///u/52te+dtwhM6cLBAL81V/9FR//+Md55zvfWeixeMstt9Df389nP/tZHMdh9erVnHXWWXzlK1+hu7ubJUuW0NPTww9+8APmz5/Pxo0bAaip8Rskb7zxRgYGBrj88suf9X742c9+RjweZ+3atTzwwAPcd999vO9976OlpeWEy73rXe/izjvv5G/+5m+46qqrmDt3Lo888gh33XUXl156Keeff37J/L/85S+Jx+OsWrWK3/72t9x33328853vfFbhqoiIyHPJ1lZAPFUyzQB1HGCQ+WSJ0sAAB+bW0diRKGlazzkBEjZCZDyPk/PwilrGMs7Mj4uJUIgNWw7QvCvOduYTMwlqQqOkg1HK46XzZglTP+IHctWjSbYsX1DyfC4Q4On2RZy/f/v0LZr4f/IucFPas8e4r3otfSNtrDl0kNUcZnlXF1valxKOBArBIMAodRxlEXM5wlgoChMDenqUkaeM+mSOilQ/lfGnIeMSzHu4wOHatqJg0JeMlpGKluEFsoTSOVp6BqlMxFmc24tjLb9dcDYHGuaRDgeoo5c4laQpw7WWw011LD16jKw7MTSpG2J7/UrqU4M0Dqapzh6lt7yaJQNHufjQk8wf6ub8w1txreXg8pXM+fGb/H0/42j41sxxuP6PJlvf38QAbyp5vrHo90jI4eoLZ7bmff8jEX7+WI79R/Ok4x7lZYYLz3RZtzhAIGDY0eGRzcGa+Q6uq8uIZ9NSaQBDTRQWTN1GkvKQYe2JP46WMMYUwotJbZXa5ycSDRrWFgVwFSF4/zr/X7GrV5U+ft1SmDn2Y6lXlL5t0V7p/7t43uSyU66ZnvqKiIiIiMjzSN+TTpXCwd8Tixcv5nOf+xz/8R//wZe+9CXKy8tZtGgRX/7yl/nRj37Eli1byOVyfO5zn2NkZITrr7++cB+9efPm8Ud/9Ed8/etf50c/+hFvfetbT3q9r3zlK6msrOTrX/86N9xwA4FAgNWrV/PJT36Ss846C/AbN66//npuuOEG7rvvPm666SZisRibN2/mT//0Twv3JTznnHO47LLLuPfee3n00Ue59NJLCYdPclDsCZ///Oe54YYbuOuuu2hra+MTn/gEb3zjG59xuaqqKr7xjW/w7//+79x+++2MjY3R1tbGddddx9vf/vYZ83/xi1/k85//PL/61a9oaGjgIx/5yKzziYiIvFASf/F6Yn/xjcLjPC4jNBIkTY05QG+sntuWXcShmgVc3rGF4g/QmYALxuBYS2woSXdbLdaBypFx+k2M+aaXwMQ95zxj2FvdypnbjxSWj9soDy1YzFs7f4jFwUz0drBAJ0sK8znW4ngWb1qw5HilPSEcMrgnGKYr4OVwc3nWHd7FSnYUtqSl8yj3V5w3Y/44MTKEWDDQzWiogupUmjyxiXVByGYIJmGyl0aeIKEhD8fz8IrvU2wtZqJXXSYSxM3lqRjLsL57K5W5OBfsf4T/t/m9zBkYxsWjkmEGCJIIRLh0xy46q6pnfG8ZiNRSVtnJcKCeL150NccqG/jHX3yJZf3+kOUjkQpSn3orz3Gni1lFw4a3nB8EZh8HadW8l24PPxERERERERE5ecb+LmM4irxEfO1rX+OGG27gpz/9aeFeiKe7vr6xZ55J5GXOv09NbNb71IicjoK/3Uno54/j1VTQ/4XD5LJTF9kcaGlg2+JWBmJ1nLXtAK0dg/hJlYWgx0C4kp6Wag4tmereFMjkWL79ELFUklVjBxgrL+dXa9ZTfzjO3CMDJes2gSwX527DUsahijYeX7SYlkNpwiOlQdN965exc0l74XHN+Cif+MV/Ec2NYQnhkCNAkozjMBKpJBGI0j46XJKpPV2/mGE3xtk9O6lgvKT8nWVLGEqW3iu4iW6aGWCsLExD8gAeVRRfI5cgSj+lywDcuWElO+ZOdRmqiI/T2j01bGownaUinuLynl9Rm/WHPe2sbKUq7hLw/KCxLxzlqYbl7G9rJ4uhPJkuWcd5XQ+x7ZrXsew9SzjYlae2yqEuZun83g7CqSRt7zyTxrnTxvCSlwR/WNGbS4YVFZFnR5/lRH43eg2J/G70GpIXSkND7JlnklPimXc/J+U49r+ek3JeTvQNVkREROT3RPb8FWTP92/KVb5kK+N/ejtJE2OoopzDDTUMh8tYtusImbyhv7ac+vExyr0E87IdPB5cw7E5i0rKy4UCDNRXkUyEuf+8KylLJKkcG8dl5n2JYrkU48wlwgBO3CU90MiuNjhjpLsk2KvvG+RNo3vZ39hOfXyES3dvIZLzJkJB/8KWny/+A/bVLsRzXLCWSw49xJreXTjWv4/f2v5tWAwZZt6cLGDSpEIukYxfxzKS1DNMKhAiG0jiYMgRwim6l1Nwll6KBo9rnvgZ94+v45E5q4kNJQmnS4M9N+9RlR2mJjt1P8SW0X4yNBQeV9LPrzecQV+slnA6w6oDHUTTGYz1WDZ0gMUjXbR9aC7UB5jfPvXRvP2v1s6ok4iIiIiIiIjIc0HhoJRIpVLE4/FnnhH/HoGu+/wNL9Xf3//MMwHRqK6mFxERmeENa6iudUj+7Z3sCc8nHYqwoqeXGHEy6RB549JTXsUlw3sJk+ecxFZ+666ZUUx1Lkl/YwzHWoK5PMlohM75ZdT3xgmncwA45GmlD0uAJI2008Ol/Q+wY3AFmCy4Bs864MA9G1bwpR9/mc17nixZT1+ggZp8DxbYUz81FOnGric5q2f6/QgnR+f0Zkw/FGulO1JBRTLD6pH9VHlJxt0Q5fkR5oz1kTB1YAO4jOHffzBIgCxljJOkHPCHQ3XKEuypn8f++lYW7+thQWcv2xe3kgv4n30i6QwNiX4u6b+3JPzMM/W5xCHFvYvX0herBSAdDrFl+SIW9nbxsd98g4pcEgukbP4EdzsTEREREREREXluKRyUEnfccQef+tSnTmre53sIz8svv/yk5nvf+973vNVBRETk5Sx70Srq713Fa4umpT73K+7+oUPFeBoHj4iXASBAnjVH9/H4vBWFeY3nEUsmONDWRCSbJRcJgbVEEkl2n9FC67EBzuw8QB0jhMhNLoVHiJZUP5XBR/nvDa+lYWSQlQf6CGdzJCNhHpu7hHMO7ymsJ2ccHq9aS9IJE8xZwsks6TJ/ONIzenbOum0WQyIUpiNWw5zhHhzP8mj9arrLGsBaxivDPNC4gtfsu5+2VC8A44EISROmLtuFM9H7MUuUNHVUMUImYOgMNzEcLmfzyG94uOFS9jbM4ZIDO4mms6zb2cFQZRTHs0SyGe64aC25OsvmPQ8R8PIk3CjhZBCXBGWMMxIJ8rMV5zMaCOAA4XyeINBbXUtFLjmxtyD0wG7SV274nY+3iIiIiIiIyOnEllyuK8+GwkEpsWnTJr785S+f1Lx1dXXPa11Oth5tbW20t7fzJ3/yJ89rfURERH4fHCpvYcHA04wEyskEggwGYtTm/OE8X7nzEVwvz/bmRUQyWdbuO8CTi+YTSuXIhgNYx4AxeK5LcCzJcFmI+xYvZd5YH+t79+NO3KjDTARvGS/Eil1HGAnEiJsIYeKcvf8QXz//NYyHyzizcz8D5ZUcMS2MO2WEnXHIRajrGaWnrZp8cPYRClKEOFpdx39teBMRLOdH+klvXsK8xDHmdg3SMVjBozsCVAV7aUx1Fr4qlOcyuE6+EAwCuGQweIBDY26YEDkeKl9FlzuHt2/5OW/f8nPGqGYvZ+B6UD/s3+Nw29J2skGXTQe3UZ1KARBllAMsJUc7c9nBv170No5WNQJ+H8ek6+LkcqzvKO0JmV/Y9FwcWhERERERERGRk6JwUErU19dTX1//YlcDgI0bN77YVRAREfm9U79rgNR4hlXswTh5gl6WdCBMOJcj4wZIBQIsOtzFnJ4+UjbIsi1HiaSyZIMuB5Y30d9cRSiZJpTyexxa43CosoloNsMZg/sJESdNiC5nIXuj7VgcnBz0RKrIG4fXPfwE5ak0v1h+LvfMOZPFh7qpGkuRjLg0J0dYN/IQY26MWO8YA9EGjFcaEHoYellAYBhWHu6i9V1LWPnetRPPzgOg6akhOj/fy6ue/HXJNYQGiHjJkvIccgQZJYM/9Gd1Ls7C5DGi2SBdrCDiDBHxUtQwxBiVWKCnoYoy0lyy+1FaxvqLyrK0cITH2UjGXcWW9uWlO98YGkf6+cPHfl6Y1F9eg10z9xSPpoiIiIiIiIjIs6dwUEREROQ0YXMeZbfsxMEjTRkhL0fKCXPj2Rezr7mNdCBE3nWJpDIcam7kjMc6iGSyAASzeZZsO8ZwTXkhGCwW8Yao4AgGGC+r5r5VKzlS00psNMH8Az2E01mGglGa0qO86omned3WB6nM+b3w4oEQYyMxanJDVHgJIl6eJFU0xQcZo54MFZQxiofLCA1YQgCct2cvNU/2k2ZJSV2OBMs50tJA2z09J7VfnMKQqH742DI2jB8lGtJeLXGCOBiqiJN1HBrjloWD3TTkj87cD6QAQ9IrI5TJkQ6HSp6/cttvCWZd8oTJ4fLTD76X151ULUVEREREREREnhsKB0VEREROF/E02ZSHgyEyGYh5hrc8fC/fuOTVHGxsASAVDhLKeoQy+ZLFHWupHo6TDQYg7YeGi0aOsnDsKC3JbixBsjjc3XYZ4ZTBONDXXMN4rIyzHtmLNYYI40SJE8jZQrmxXIbKXDcpKhmlARcLOKSoIEOUDFHGJ3r22dIa4f368IzNfOLpDIu6O6hIJ2Y8l8chgDdtWqTwe4YQFPU3NP5a8HA5GqtiR2MLnuNgrOWcY5YzxgdKyhrEH4HBWoe1uzt4+IzFhefq40Ms7dtF1s2xv76d29/7Dt78/jkz6igiIiIiIiIi8nxSOCgiIiJyurhxGyNUM4fOkskOcNahfRxsbKE8leCcAztpHhoiYAwJGy3Mlwm5DNXFcPIe4USalQOH2NC/BwBLhDgxemhhzT6//LP2HOCnF25kpKKceEWE2vgITXSRIoot+hjqkiFIkhApsoTJEMIFMoSfcZMcm5sxraLcYfPjv2I/q4gyThOdgCFBDXknT5U3gItXCBod0uSdFPe0b+DMrqOE8qVl3rt6FasOHCwEgwDWGB5pWUbjgWFiDBH1xhmkjn0sBWA0GmZ+3yBn7HuUg+cupHX7Xi6NdBO46c8YWzWHeg/eGdCN00VERERERETkhadwUEREROQ0kd4yiAUszoznsoEAFakE191xI1VJf7hPC+xnHgPUAOAEPDAGL+Ay2FLHkgMPkCGMSw6XPKNUQ1HZkUyWNfsOcf/aVdRGesjGIthRcMiTn/gY6pAlhH8fQINHmCQGjzwRXPJkT7A9LmkCm+eQnjb9ovPDVCf7OcxaRsmTpIYF7CNGAjwYCzZyW9tGAl6GllQfWRvkUMU8csblSDTForHeQlkDFRU4eHRXVRGwOc7o30NDeoi+UA2RVBDHCzNOM12hKAdCLeAZOubVs29JG/Ubqrj8T+o4D4CFAOQm9pAz8xCIiIiIiIiIyLOii25PlcJBERERkdOE86pFuDf2MEQVLUwFYBbwjOXKLfcVgkHwP2JXuyOk8mXkcUgmgkTHUiRiES7Ys41g1iWLSxYIkC4EfsXKkymW9u5j/dEtPNW0ljwGlwx5goAzsXSpAFkccuQwBAiQm+hBOFQepTLfQzSVJ0CGcFOO5N+8a8by9bUuXZvOouLOATwvQjNHcYoGJI1lEyyId/NE/XKGg7VMfpmIpHL0B6rJVgSpyo6TdoKUZTO8YtvTZB2Hs4P9NKSHAKhOZElMhKYA1ZkEXqvljtVnUhlPUdc5xLI/05ChIiIiIiIiIvLSo3BQRERE5DQRfN0iKlY8RXwndNFEjDhh0pSR4sK92zDT7sU3TBX9+SYCQACPABmWPdVF/7wYK492lMybI0SExIyhQCu9ft75yG0MB6pZ0DFON4twyNHIQRzymIm7+hWzE1NT9QEqo3nSG9vJ/tG5HN6b49E9sGz8KOvWRai7ag2EZn6c9fKWo0MhXuM9xiFWECQzY56Vg3toHD/KXS0X4BkXoHCPxQMNTRxrqqE8kWb14cO0poco8xLUTgSDANlZhjxtiI/RV1NFf1WMswcOcM5qfdQWERERERERkZcetViIiIiInCaMY6i+4w2MfGYre3/aRW9tFRu6drC4rwuYDOWmetj1U0/xEB0ulopMmtq9Y7OVTjljZJwgaS8KWKrpZnG/HyLW5QcoJ8cxFjBUXsVjDa8k4o1T3pvivNSThfVawCNIgBTeq1eT+Oc3AH58uPEc2AjAvBNuZ3I4y5q9j0/MuRNLBAiVzPN49VqOljURzHqkQ3446BnDvnmNPLlmYWG+XHWedfftwLG2ZPkgGbJES6Ydqa3zt8Fx2LegGS9nwdUQJyIiIiIiIiLy0qJwUEREROQ0YkIu8//PWsrfvhT3p0fpuj9Ly9gQ5akENuAyGq4ilIyTDzhks0FMaSaGg2WcCBknQMjLFT3jESBHo9cz8ThHoHAvQXDxCJBlvKqM+1asxE7cdM9p8cjsDnL2yE7KbBowuKT9eyPu6uFURGuCVCYGCuuGFBaDJUjeOOyILeZoWZO/fs8QS6SwLiSiIXYsmxoK9P0P/IiL9vsho8WQJ4g70bsyyhDjTox4qIKh8nIcz+OONWcWlh2LRTn0nX0sfe/SU9oGEREREREREZHni8JBERERkdNQw+IojX+5mPp/Oov+/ktJHRuFijCBijD3fWwb38+1cMW9T7Li0LHCMhbI4pIOB0gFk0TiHh5hsrhYDBVF5RsCWIKYiYAQIE4T29rmF4JBAM91ONTcRtLzl16S30dzrptYepBQddkpbdt4Z4K63NS9DA1gSNITirKrfAVHo+0lzznWsKinj4cWzyMTCgKwvOdAIRj057N4Jk8iECaWTeEZyw/P28Tu5oVg/AFZk8FgYX7X84ju6QMUDoqIiIiIiIg8H+wzzyLHoXBQRERERKC5svDr3IVh6h4e446Nq7DA0o4eUuEg2AyN2cMsSe8kmPZ7De4NLWF7+WoA6rODtGT6CGVyZAhTSy81jBbKzRMiEwgynedOhYV3tZ/DlnWLeP/932X+/37FKW3K4W8foI0AbtG9BseoJ56ZT50xbFtUzZGGOqpHRlh++AB5J0h/eZTWsQFqRsYYqorRPjyz12LQ5glmRwDoqm5jd8uiwnMOUJHJkLWGuqER+hpr8ZbUnVL9RURERERERESeT84zzyIiIiIip5PG17Vz8c59tPQOccemVXzlmlfws1es466LzyJQGSeIHwx2xNrZHV1ZWK4/WMvBaBsPtS/n5k3nkydKnqkwMMQ48/tnhm7ZgEN+8t58LljjcOvqy9g9HJ0x78nIetDJQiyQx2F3ZAW9LMTg8uMLN7Fj4VzGKqJUjCQZCtUwGq6gs6YGk/e48rG7aRwYZG/dnBnl2qJrEg/VznweY3j1PVtYvr+TcCZL/+r2mfOIiIiIiIiIiLzI1HNQREREREqEW8o457/OofU/95PpOUjZeQ0MZ2HnU/DjC65hyeBh2hKDhFc0Mcc6HHkwSX48T3NNguU9uzkw0gRBw+0rNrJpTwUV+QFcsvQFqmnt7yfnuuxtbCHvuOSMgxd0GKorp7F3hN0L2/5/9u4zTo6rTvv+71RVx+nJeTTKOdiWc8bY2NiYYMCAAQM3LNikJS4LCywbWJabsMAu+7Dg9U1miV7AJtgkA7acIw6SLCvHyblzVZ3nRY9GkyQrzkia68tn8HTFU9XVo+6+6n8OAGkvRfvGLMsuqT7k9rde1cza7y4l2h+yvbIeP0jRSh/t1ZV01FQBkBrKUpbJjVmvO15NY0eWi7c8STpfzTYWMpstOITkiZElRhWlsQxn9+6asN9kJkciV6C3PE4YcVm47PC6RRUREREREREROZYUDoqIiIjIBGVLK1j6udPHTDtr5LdlY6a3ADa0GMdQAFo7hpj7v39hsM9ny/KzqfrpJgDCAB6f00yQ8LAWrAXXhqV5cVi3upmhilK14OJtbdS9bux+DlZtPCBb7ObxqqVko3FaMj0AOOG+yj9jJx+Z4PazTmfrrCZe9esH2M082plFjDwZkuTL4eKh3+PakAU9Ozjj2bU8umg5GINX9DnvkWcZKIvTPquOV7ypmspq97DaLyIiIiIiIiJyLCkcFBEREZEjZhyz70FDiuCdF5IEVgQhbX/aQdhTwLFQli3QG48SOGODs87qanY01FE1OMjyTXtotYMsu6Tq8Bozv4pZpPEGDM/UN9MVL2fOUDf1/QO0dnSxs6GOwfIk2XiURG7fuIRBBLa2NgOQjceANAERMsNdo25ubOGuC9/Pmdue4HnrnuCadQ/Q0p2lnzhVfWmGqhPUvGQeF358BZGIeu8XERERERERkeOTwkEREREROWaM61D74xfS+7Y/4m9NM7enB+sFdEVrCb1SQBhxAs7e8TSvuq+TTFhOrDGk5s9vwBxmwGbKY9hPXor90KMk8wUysSidZQnmpTt5w5rf8eDSJeyuqabZ7KZmaJD+SCWV+Qz/e+HzR7bx2Mq5NHf04g5XG+YjHhsXNjOUSvKblZey/NbXU17lcialqsmgaPFiCgRFREREREREpo557kVkUgoHRUREROSYip5aS+ODryIcLGBSERbmfNJ/3M5ANAE1SRpPrcDtP43o757A1qQoXLYKvCPrktN502k4n19Pc8cAO+qq+UvDYoqdERakt/Pyp2/DpQhAgEM3iwmIU1YsMDC8/u6mGv73Refw0vvvpz7dw88uu4pcMg5AZS5Nqmzfvoxj8GL6QCIiIiIiIiIiJwbd3iwiIiIiU8Ipj2KMgUSEsqsX0nx5C81nVOF4Dra2nPxrL6TwwtOOOBjcq+LjZ+JimdvZw/plc+ivqwCbpJu5dLkN9DiNdLGcgFLod/mTT+CEwcj6nlvg7MFHmG13kE9ESxOt5ZKnHsP05Y5KG0VEREREREREppoqB0VERETkpJR87SIiK6vJ3bGD62vgz2c+j8FfV7Bkzy4qK2aRemYn4ai3wyt27uTtf/g1bQ1lpPJZztv6BMlinqcaljKvo42OihrcMGQokYS65DQemYiIiIiIiIjI4VM4KCIiIiInrcgptUROqaUceC3Ae6/cN+9t38HetoMCccDQmyzjwQWraBnazfI9a8l6cdbMO5M1C88GY/DCEKyle+VsMOpGVEREREREREROTAoHRURERGRGyn319biDX6fijxsJgduWXUUYWnYmm/nJimYWtW2np6wSYy3WGEwY4tqQ1e9fPt1NFxEREREREZnxLLpx93ApHBQRERGRmSniEfzo7QxlC7TvKnKO57Hhrl4i/Tnmn1NB67nnAXD6s2ke+1U38boop19dR2WN3kKLiIiIiIiIyIlL32yIiIiIyIzmJKI0L4oC0DqvacL8xsVlXPX+sqluloiIiIiIiIjIMeFMdwNEREREREREREREREREZGooHBQRERERERERERERERGZIRQOioiIiIiIiIiIiIiIiMwQGnNQRERERERERERERERETjBmuhtwwlLloIiIiIiIiIiIiIiIiMgMoXBQREREREREREREREREZIZQOCgiIiIiIiIiIiIiIiIyQygcFBEREREREREREREREZkhvOlugIiIiIiIiIiIiIiIiMihsJjpbsIJS5WDIiIiIiIiIiIiIiIiIjOEwkERERERERERERERERGRGULhoIiIiIiIiIiIiIiIiMgMoXBQREREREREREREREREZIZQOCgiIiIiIiIiIiIiIiIyQ3jT3QARERERERERERERERGRQ2OmuwEnLFUOioiIiIiIiIiIiIiIiMwQCgdFREREREREREREREREZgiFgyIiIiIiIiIiIiIiIiIzhMYcFBERERGZJlu/vo67/pAha1wu3PQX5rftJBNJ4DVWUPt3FxC+ZMV0N1FERERERERETjIKB0VEREREpljuiR4G3/gzfnb6RSzb1cnZW9ZRU+ylLughxCPXX0HxrbsofuFlRN5w+nQ3V0REREREROS4YzHT3YQTlroVFRERERGZQja09L7+V+yJxlj16GaqdhV4NrqQB8rO4sH4GVgsHnkiNiT4u99Pd3NFRERERERE5CSjcFBEREREZAoVNw7Q0r2ezkIVZZnimHmd0Toecs4hRwSAskKW7g3p6WimiIiIiIiIiJykFA6KiIiIiEyhaG8vd88+l8A4k3aAUnAi7GQ2AP1OimxnfmobKCIiIiIiIiInNY05KCIiIiIyhXqCGINuOcnCIIEDbrhvnrGWiB8yaFI8VrWQTDHB8hXl09dYERERERERETnpqHJQRERERGQKmboy+qqT1PfkGEpFCYfLB50wJJXJY4CkLVA3lKW9oYJkdWRa2ysiIiIiIiIiJxeFgyIiIiIiU8gphvTUlFMZpmltG6CnJkaymKNqKEs0CIlRoJIhUsU8yVyBQn9hupssIiIiIiIiIicRdSsqIiIiIjKFTEOc+q4+ikRI+QVWbO2ikDDMohswuJT6GQ0x+BjcmDu9DRYRERERERGRk4oqB0VEREREplA+C+c/tR4DGMCzlkQmIGuiI8EgwM6KKmLZAC+Tnba2ioiIiIiIiMjJR5WDIiIiIiJTqMYr0FPIkyE6Mi3AZbvbQLlNE/F8+qJJMjZO+UAOisE0tlZERERERERETjYKB0VEREREplBx+xCV9JKhfN80z6G9NUXLwFaW927C5GGX00Jn2IDty0PjNDZYRERERERERE4q6lZURERERGQK+eUJaujBJU93VYr2qhRrFzexoLCNU3qexbMhLiFzwp2s4BlM2+B0N1lERERERERETiKqHBQRERERmULx6ijbmMOumgbWLmth1o5O/KjH3IFdE5a1jqXwrtspnL2A9vnNFFsrmX1VE8lZyWlouYiIiIiIiMjxw2KmuwknLIWDIiIiIiJTyGspY5BK9jRXUz6YxViLtZahaNmEZbtilXT0JzG/7cIx3bTNqufB27q56u/mU39+3TS0XkREREREREROdOpWVERERERkCgU7BvEIcYMQ33Poqa8gjLjcNftseqP7xiHMOxEer1pKtOAQ8UOixYDFW9to6hrgkS+tm8YjEBEREREREZETmSoHRURERESmkM0WKSNPQ2cPm+c3jnSDUohE+eniqzh951qKjmFrqhUTGiAzZv2W9h7WVTRy74pbqSsO0ZztIVhYRcW3X4ozr2pkuehnf8Hg9x/lmdgcqnN5yupjVL5hFdaL45zdgrOsdgqPWkRERERERESOFwoHRURERESmkFsfx8Fi4hPnRWyRmv4iA9EExXKP6mJ+wjL5WISsE6MvFWeoEKEvnmQoTBB5/d2UhSGLXzOLpl/+jk3tDk9Wn81Ln11D1PrYNgg/sp4+asmSJP/ihdR880VTcMQiIiIiIiIicjxROCgiIiIiMoXcdA4fgx+MHTh9Qc8uLt38GA6WpgLM276bHsrxyFMgwRDVBMZh0+xGart6sRaGKhLkwijxYoGu6hoyiQTr/lTk8i1V1KaHOL97PVhDiIsdHlGgkn56iBL91SZ6H+ig+tyG6TgNIiIiIiIiIkfIPPciMimFgyIiIiIiUyhsrSHSEOAN+URzPoW4B9Zy7o61ONiR5eIMMJ9dI4+7nFruazib6x79A6ELv5x/NmDxHYehSIyqjj6aEl2U5QJq0xmGKGMwTNFDA0nStLCbCD4GSDLIINVs+c4mhYMiIiIiIiIiM4zCQRERERGRKbalrIbm3jbMOodijcF6hlQxNzLfYomQBqA/nuLp5iWU5TOc3/4EUQxrK2dT8CL7NmgMmUSMRW27aMhkKRBhgIqR2RnKaKOJ2ewsLe6F+KHDup4IZ0zNIYuIiIiIiIjIcULhoIiIiIjIFAo7hgjTIQuDzVSERRo7e/FxyRMlRgGAABeHkA318/jvC15PcTgIbOnt4K133U7WjU7crjF0mXpm+5sZpGzC/CFSI7/3xMr53bLTSO0eOEZHKSIiIiIiIiLHK2e6GyAiIiIiMpNknmjHy1k64/U00svuqlo2tMwmE4thCXDJYQgoEOO2VZePBIMAu6sbuGvhaUQyFqwds91IMSCSD9idqsUlnLDfCEUAsk6U9WVzWLJxG6mB7LE9WBERERERERE57qhyUERERERkCsXnVJLzYjw8exXWi7ChaQ4AZ297nBet/xMGiJJmwK2gM1U7Yf1d5XX4KYdkpkgu7hE6hmghoKo7R1dDBWShJ5Wgrj2HtS4AIbB2UQtDgyFZP85gpIzWdAcX7nqYYtOfGfIqcCorCF5yCt7fnoupS07hGRERERERERGRqaRwUERERERkCrnGYaA+ycL+Lfx65QsA8AKfyzbeixm1XEUwwCk7N/LQglVj1o8NFcAYYsWQWLHUDWl8sEgyV2ThQCeOC3etWMLGeqgcypFLxNndVMNgeZLH7GKuffT3lA+mac3vwQE6nHr6ipXEO33qvnkfwe+exXv0xik6GyIiIiIiIiKHx475FC2HQuGgiIiIiMhUmlVOba6f0NnXw3+ykCHuFwhxCIhhMXjkWbZnC5vrW+kur8INAqqHBqjqS0/Y5PziVpYGG4hQIB1Usaujgo1NzWye18Rgxb6xBq1x+NWK5zGrvQ1vZ5HtzixyXgysJZ4O6BisZcnO7QT/uxb32hVTcjpEREREREREZGopHBQRERERmUK2N0tzRze1btvItIF4Od2JapJZy95hwQNiLGzr4P903UHcKRIJikSDgC1lTawrnzeybszJUGgwdPfX0DjYRRkDXND+EGXZ1TxYtoRBUmP2Hw18cpE4a+adwalb1zNnYBd5N8qG1HzS+Rg9hQrcT9/PwLzZzKkJ8WaXYzwNVX6s9GVD/vM3RR7ZZhn0wAkMTTZgdXnI4rkuzzs7RlWFzr+IiIiIiIgcPQoHRURERESmQBhYclv6qPnk/7Iouxnfg1O2Ps1Tc5ZjHYft5XNYlt0xag1DBJ+k75MgNzJ1XrqN7bFG0tEEXY2V7J61mIc4DYDWne1c8uyDLMk+Q8PAEKs3b+KO+rHjFq7asZXOeIr5/dt4fvcDI9MXDWxja2IeLXTj7ShSeMlaesuqiQ35DNU1Yr74YhIvnH1Mz5H71DYwhmDlnGO6n6OhpzfgT386n9/8YRPlflDqzCbmUKyLES9zWb3MZcHCKN0DIdmhkBecFSMRH9vlzZ7+kLd9McOmSJRex6HVD4hi2YjD+j5DsNNg1uSZWyhS6weEQKfr0BVxiZY5UBkhmwHXtyQTMK/O8MpTHa5e4U7HKREREREREZEThMJBEREREZFjbOsjA9x9004yQ5aqzApeXraLZKaIHXSZvXEXgesQSxcmWdPCuDEUDFCez5Ihzp7m+jHzds5q4InOFUTDPJtS1ZzZ8Sj2McNDi5ZSdF1W7tjGWZue4eennsfp7WsBKBDBAhGKLMpuACAkihtESQx0YoCKjj78N2yk951X0retCNkcLWfHcForKLz4LGx54rDOi+nop/I1n8Nbu3Pk2PYedf9/vo3idRcf1nYBKPrguWAOfgyK/v6AP9/eT/qezczavZ17Zp1Op5tkYyKGW+PxnhfFuWylRxBYPvuZfvqdGE1+ETPc5qBgcXZl6YhHubk9SvbuPHVBQDwIuf1/Bwib41SUOWzIu/QOWExoGXBdhhyHyjAkOtwOH/ANrMzkSIYhzvAxuEBzEDLoumwteFS0BbSEYWmlIdjQ4/DebQ7/+cMhhqIe28rjFK2hOgH/8ny4dqk9/PMpIiIiIiIiJw2Fg3LI0uk0hUKB6urq6W7KcSUMQ9ra2mhpaZnupoiIiMhxJD8U8If/3IlfKAUzZ21+lrK0S96JcWr3draW17GjvJ6t5U3MT7ePWdcnCoQjjz2yuOQ5L/MQD6RO5S/OkrE7M4ZcPMruaDNhbci3TrsWJzSct2Edq3ZuwaNIZ6qCqvwQJgh5wj2F3aaFheGzLA6fHdmMS4EAB4vL3sjO4lL81iMM1FTQUVXJhl9mecnan5D8px/T99t/wIQWk8kTrJgNu7tJf3UNsaoIuy45lyaTIRnxCRc0Qa6I6UtDKk7Vlf9M2D7Avs5Uhw8DqHzP/2OwGJJ/wyWHdL6ddTuovuqTmOy+sDWsTNDzxJchEd3vehufzfPf/9XHqTvXsqBrO3cuvYgeJ8mtNVUMei4U4Z5bLV/IBsx3CjyaTDA3W8AAac+lLxbFGkNoLY8n41SEloogoGAcUsaSiUWJdvm09TgYz1JnLRhDdRgy2w/odRxyjiEwhizwvMEsLqWQcMxTDMwp+uyMRmgI910b7Z7LnphHYAyPlScJHQOBAQe68oZ33gEfutOS9w3GGIqBBd+CtXgu+BgijmVpreGVy+CtqyGhT4vH3PBlICIiIiIih0Vvpg+XPu7JIVm3bh0f/OAH+Zd/+RfOOuus6W7OcWNoaIh3vetdXHjhhbz97W+f7uaIiIjIcaRtQ2YkGGzs72Fp2w62pBpYX9WKNQ6hsVjHsKeslvvrl7G8bzteGOAGEOLSF4kRhpaKYJAYmdJGLZzd9SR/8M/H9yIj+3KCkLJ0jkwsyR9XnDMy/Zdn1RJ4AY1DfczpbuOatWt4smwZfYW6UrvCsaEkgMGnl1ocQhwsUQIasgPU7+qnuGsnUfrxsIQ9A1Sf9bcYLIORctpSzYRR6E7VsGbB2Zz7s59yxoY1hBgcLAXXIxr4OAxX27kRDMEk+4eyT/7o0MJBP6Dmsk9gAjum5tL0Z6lZ9m56tt0MQDodsOauDHt2+lTXOOzsDHjqmTwFG3Da5of5/gXXY43h2USsFAyONMrwiTsCluUDdkWjnDqYwQd6Y9GRhMcxhvn5Io+kEuSdGAARa6kIQ2r8gIgN8YKAonVpDAJ8Bx5JJck7DpEgpKboMztfIDAGx05e6VdwDGW2FN0mgwAfy85UfGR+CBBaiACOgcBCaEkHlMoPHVP6iVgohvhB6WQVcXiqy/DUPfDJeyHhWrJBaYMRY1nVaFjdYLlmMVwwG7I+rNkOsyqgqQwyRUhEoDwK0ZO8Z9PQQncWhoqGxzrhO+s94q6lJmbZOmhoSVniBv7SbVhZa/nwmQFzyuGXmw3vu8sj65vha8aChYgDxRAcx/CKhT4vmhsyr8LiGsOiKkvsJD+fh8uWLuGT/noTERERETnaFA7KIdm4cSOdnZ3T3YzjzsDAAGvXruXCCy+c7qaIiIjIcWKo1yeecqlo3FetVp0eJOtGWF81GzscJoVmX83c5ooWNle0gLWct2UL5TZLXXGACP0Ejh1dREgsKPLSp+7klysuoxiN4BV95m5pJ1ossnbZ/H0LWosBfnPaBQCUZ9O84b7bKeb3hUl5EysldaNkSFJDF2Z4RoEoIXEMDoYIHSxloDogTj8Le7fiYKgsDlHZW+qalHY4d8sj3LHyMjKRBKliFoB44I/swwDRoDh+1yP6ChGGzv4krTu34kejZBa0UrljF04YUjhlLoXLTsHtGgDHoXjJKoJiwO1LnkdPspLFXds4e/sTONbiAPl8yLarvsZmanlg9mn0J8pHngOs5ayOTaxq38Tu+vl89HdfYW7PLv768jfzwCkXjWlT3jEsGRziHL/0ZAxFvAmlX9viUZKhJRX6pB2HnOPQ7bl0RzyifsB5Q1kSthSIRkOo8gNyjmVJrlCqoHRdOmOGMj8gGYRjqipDoM/ziABtruHUfMDm/VVEOqaUnISjznBoIWpK/y0G+553Y0pJS2RfV6zZwEAYgutQdAyPdYY81u3wzfXD64elYAsLeM7Ym3YNpCLQUlYKaiticNnsgB2Dhu0DhqQHNQnLH7Y7ZIuGllRIU5mhImZZVA1Lqy2pKGzrh1PrLb/a4tCVhe604dl+Q8SB1y0LObclpDdneKbH8NNnHWIe/ON5AXVJyz27HH660bBj0NBUZllYadnQ63BBc0i/D3/a6RJaS8IpdeVa4cGCquHr3Ycnuw0xFy6bE/JsDzza5VIIhs/V3r5k954/B7Bm5Njp2PsLPNMPP904Or3ad93tXb84nI+HoeV/N3r877N2zGsy6cHfn+uzusHycLvDIx2wacDhwuaAxVVQn7DUxiy/3+kyu9zyojkh7RlDXwEeajfkQsO7VwWkItCegV1pQ2vK0piEv3QZblrr8nS3IV0sBZ4xBy5sCfjw6QEb+gw/3uiyZo+Db8EJDT7QlAzpyxu68oZ9J2Tvf0u/VsXBDw0ZH8CSdC2VUciFhnxQqrRNuEX6CnECW1rbMVAdLW3DcaA1GbJtyNCdc8b2Pezs21XSs5xSFfB0n4u1huYyy18tLXJec0hF1DAnNflfmbYMVMUg7o6d1lcwLK4oheO5AOqG/1wOFkovqZ9sdmhMWF42z1IMYXN/6bqYV27xQ9g0YGjLGE6pCWlIgmvgwQ5D3oe6ROkS2jZg+PMeh+ak5RXzA2rjsKnfcF+7w6604bEuQ2fW4ZSagE+f51MIDZsHDKfVhvx+p+ErT3nsyRiqo1CXsGwacMgHhrKIpRhAb94Qcy1zy0Pml1v6C4ZUxHJqbUhltPT7z7e4PNJp8BzDmXU+xnF4otshH8DsVMiKKsudu11cBz56WpFrF4VEhv8gBSFsGoCmJDzWZaiMwuq60nnenS5dY3NSlv4CRN3SuRktXYRHOw1LKi2OA9sGDatqLR7wg40u5VHLyhrL3JQl6sJQAbpy8Fi3Q9Kz9OYMS6osS6ssAwUYKBh+t9Ml48PS6pB0Hr630WNBRcgNywPW9xlCC7/f5bJlwLC00nJ6fUC5BzuGDLduK10EiystV88NuLAppCYGPTnYPGj47Q6XnUOGEDizPiQyfN/FrrShIwutZSEvbLXMSllmlZX+lG4fhO684bRai2OgNw9P9jhkfPj1NofNA4ZV1SHlUXjpvJBTay09OXAdqIyWlt81ZKhPWhqfo/fu3UOlfc0rt7gG9mSgLgbtOUNdrPR3uGb4Ot42aEh4loZJtlkMoSsLPXlDwoWKmOWZXsPKakvMg19uc3i6x+HFc3xSEcOWQdiTcXiww7Ch3+GsupBPnOXjGOjMwo4hh8GCpTxmqY0ZllVZXAf8EHYOQX0Ctg4aurKwur709yG0pddhQ6L0T4uIiIgcG8ba/dyOKjKJX/ziF/zzP/8zX/va11Q5OMru3bt52ctexg033HDcVA52dg5OdxNEjjljoK6unK6uQfSvmcih02vo2Ojakef2m9rp3lUgVuZw0atr6Xu4k7WP5KkZ7OOyvzzKI/WLR5a3QD7ijAmYagazzO3oByBGjlxFiOvlOK1nw5h97UnW4eUi9CQryIQJXBvSkO/lBxdewe7a2uEdWMZ/t7awYztXPvgg65xSO6rCHs4J7scdTh9DDHlSxMmPWS9HGRDBx6OHeiJkaGEtY7+xn2jiyImjjqG8nqbBzv3OD4zDlqpZ1OT6qc4OTLpcCOTcCP/wio/QHasYmX7hpod52/0/BiCPyydf/EG6U7Wjtm0IHYMX+Lzvj19ndt8eygsZLNBVVsOPV1/NfXNW0RaJ8FR5GR3RCK35Ai/u6hvZRt516Eju+4Y1O1xxOLqdXa5LxnXAWhbkCyzOF8e0f9B1GHJdEuNeiEk/IBaGlPkBedeQdVx6PZd1sQhZp/SsGmtp8X12JWITT0zUhUI4cbprSt/MTpjulL7RBsb8UQhDcN3Sk7j3B7Pvmh297OhpE/rLHDfNHujK2Lu955qxv22MuxYdM3HW3nBp9KnY3/4O1Mzxl707buFRIeC+7U1y7sbva39/l10zybndu8LegNKODSsnNLY0Me5acoGZfJ+GyZ/H0ad8f23cm/JNmG8n/XVfkybpR3dkufHP9d4QcvS5nGR94IzagP+5vEDtcDiyoc9w411R1vY6VEQsHz2jyAtbQ679bZStg6XXVsyxhEAxNJxdH5APDE/07L1o9lYK2+Hsff/Xo4sdvsyeq8urA70eRj9vB/G6OSZKN5p84FSfhGf5zGMRAju2PQ3xkPObQm7b6g4vva/dq2tDvvuCPI0J+OeHPb7ytAfjlhl/fQKUe6XnIT2+j+VR7Xru83I452xiWw5l3YRLqfKaUhD7zhU+X3rCw7f7315NLKQ3b3BNKaTc0G+GzzGcUh3w3RcUaCkbu87mQcP/+VOcZ3pKj53h682OOSelY7m0JWSgAI90uTjG8poFAV+8oDgSwN261eUj90foyY9/nU12fR/s9TqWi+U1C31+vtUr3YAyajuesbxtuc+vtrnsSDs0JSyfP7/AlbMn+fdK5CjR5yGZKvX15dPdhJNW1rznqGwnYf/zqGznRKJwcAZ57LHHuPnmm3nyyScBWLlyJTfeeCNnnHEGUKp+++IXv8hDDz1ET08PDQ0NXHHFFdxwww3EYjFuuukmbr755pHtNTc384tf/OKg93/TTTfxjW98gx/96Ed8+tOf5umnn6ampoZrrrmGt7zlLbjuvts1BwYG+NrXvsYf//hH+vr6mDVrFtdeey2vfe1rMcMf/m666Sa+/e1v86//+q985jOfIZvN8sEPfpCXv/zlDA0NcdNNN3HnnXfS19dHa2srr3vd63j5y19+WPv44Q9/yBe/+EUeffRRXNflec97Hh/4wAeoqqri4Ycf5h3veMeYY73tttumfexBhYMyE+iNvMiR0Wvo2Pju32+ne9e+8e4wcM0Hmul85y9pHdxD+WCWe6tPH/sVt7XE/SIFz6UiU6Cpdwhn1HOyoaUG4/q8ZM9dJP0cUAq2Hmpczcq2nRPasLZ+AbdccDEYQ2VmgMHE2A+jyXyWv7vjWzzsrWLQlMK0VDjA/HATDpaYDamgH29cd585kkCUPFH6qSFKmmbW89xJAQe5zGTL713nwOunI3H++jWfHLuFMOBzt32OunQvTzYt4SuX/NWY+RbwnVLQUp3uJxdN0tLXxhsf+gk3X/AGulM1Y5Yfch22xOOsTJeqIAdch4LjYF2XXKTUKcuuiEdPZGwHLXljCLC0Fop4FmqDcML8rDexX8RYEJIIQzZ7LlHHIQB6HYf2yNhly4OA+jBk8+iA0Bmusgr2Ez6F46Z7Tmkdx+wnsBoVwLjOvrBtsmBwfybJCp97+UnCoEk3Nt64jRszts124iIH3OxzfQc+msPEEHRM0ycJD8ev/1zfhe83IBznYHKNw8msnuu5G30OJgsIxz+VY87XuHkHCiAna+t+lrt+sc+XLigF81f8MsZfusfeNrG0MuSZfpUpHZzDCyhfOd/nw6t9zvtZ7LDWP7Edeaj7wtaA772gMGbaS26P8mDH4fer+4XzC7xxSUBHFs64JU4hnIrn5eCD3KRn+curc1Tuf8hgkSOiz0MyVRQOHjsKBw+fuhWdIf785z/zt3/7t7S2tvLWt74VgJ///Oe8853v5HOf+xyXXHIJf/d3f8czzzzD6173Ourq6njiiSf41re+RX9/Px//+Me57LLL6Orq4mc/+xlvectbWLly5SG3w1rLu9/9bhYuXMh73/teHn74Yb72ta/R3t7Oxz/+cQCy2Sw33HAD7e3tvPrVr6axsZGHHnqIL3zhC2zfvp2PfOQjI9vzfZ9Pf/rTXH/99RQKBVavXk2xWOTGG29k06ZNvOIVr2Dx4sXcc889fOpTnyKXy/Ha1772kPYRBAHveMc7WL16Ne973/tYu3Ytt956K/l8ns985jPMnz+fD37wg3zxi1/k0ksv5dJLL6W6uvoIn7Gj42C+LxA5ke29xnWtixwevYaOvqE+f2wwSCmD+emX27HLzub56+4nXZdgZ6qBWbs6SgGgtczu6ac6ky2NwTfJW3Q3CBmKJfjZrEuZV9xD1PpsqJ5LdX8WGBsOWmB2ZyevWrOGh+cvprbQwWPzTxmzTGtvOwZY6m9h0JTR4dTQ69TwpHM6AOVhP8uDZ6mhZ8x6BoPFEiNHOX1UsP0gz4wZ9/uhfvvy3OsnizncMCBwXLygyPUP38oFWx7FDUsBZ3+y8oBbzUSTOMDuqiZuuuCN9KYmvp9LBSFLMlkCLH+ormRXPMbSfJGqMMQNQ0Jr6XEnfknrWctp2RwuezOpsS+6zohL6DhUjAsNPWvxAddxCIZfqAVn4gs24zgsyObYmkpgLVT4AXMyWQJj2BnxGNgbPJpJTp8xEB/uGjW0kweDsK+CbG+XmofqsP7OjF/pcK6dYaOryvZ2ofpcu3uu6fvd12GsM2Z/R3Ccx8LhPnd7z8P+isMOtL+DOfxDPM/3tjkYA315JgSDgILBQ3J4F/h97S4/3Rwe9vontiM/5rv2OGPes2V9jigYBLiv3eFNSwPu73CmKBiEA5+LsfMyvuGhTocrWlU9KMeGPg+JyEymcHAG8H2fz33uc9TX1/Od73yHVCoFwLXXXst1113HZz/7WVatWsWDDz7I+973Pt74xjcC8PKXvxxrLbt27QJg8eLFnHrqqfzsZz/j3HPPPaxuRcMwZPny5Xz+85/HGMN1113HJz7xCX7+85/z+te/nvnz5/Od73yH7du3893vfpdFixYB8KpXvYqvfOUrfPOb3+QVr3gFS5YsGdne9ddfz5vf/OaRfdxyyy1s2LCBT33qU1x11VUAvPKVr+TGG2/km9/8Jq95zWsOaR9BEHDFFVfwgQ98YOS8dXZ28sc//pFcLkdtbS3Pf/7z+eIXv8iiRYu4+uqrD/m8HAvV1Um8Se5AFzkZ1dbqDiyRI6HX0NFTVRmSrHDJDOyruAtHda23ZuEZtPZ30NFUS09tJYlsngU726ne2TZqK2O/8S66DunhMeWKTpRtppXd8xuI5QrMXTtAmjhl5EaWb6OWTfFWir5L68ZOEtUDnLrraZ6YVbqxq2aoj6ufuheAwVSShqEuNpgFY45j0Klkm23FhFBFDwEuPnEMLnvHIEyQwWVv95gHUzF4qA49YTFhCI7Ly5/4Hc/f+OCYeSv3PIMX+Pjuvo9AIYAxOOHYcf16y6r2u4+YtTxQXsbmZJzTs3kqhivwQscpBX97v2QatU5Lscjed2UGCLHkjUPEWrLG0Av0O7AogDiAtcTDEMdauiIeMcALQ0IgZaDXHRti1PkBEQueMRRcBzcIqRkexK624PNYKsHQ6K5rRzcu5o2d7pfGGDyg0U/N/oK2A61z2I5yYDY+hDpQD6WH0vYx25ykS9EDcUc9F8dLPni0e7Ecf80cy29kR2361EaXurpyqkNLU1lAW3rsouURGBzb468cZac0OFy2KM7n/qKg53AsrjbU1e17z2atZXZ5wI4j6DTojFlR6urinBWWbk863hjgrLlJ6qqV3Mixpc9DIieu8TdeysFTODgDrF+/nvb2dt7znveMBIMA5eXlvOY1r+H/+//+P7Zu3UoymeSWW26hpaWFCy64gEQiwT/+4z8e9fa8+c1vHum2E+D666/n9ttv5+6772b+/PnceeedLFy4kLq6Ovr6+kaWu+SSS/jmN7/J3XffPRLcASPdou519913U11dzZVXXjkyzRjDJz/5SXzfxxhzyPu44oorxuxjyZIl3HvvvfT19dHU1HSkp+SY6O3N6M4nOekZU3oT392tLkBEDodeQ8fGha+q5fff7Bg5p8YxI78v7NrFgq6dPNs0Fz/iMRjxWLt4Dsu3byeeDWmvq+KhlYtYuWEHdT2D5D2H9qoy7Kh/1GM5n0jR0lVXw70XJunaUM6yzu3EyNPrVfBU41x2LKgnm4xRMZDhtU89xRVb1nDFunsYitTQ0lca3++x2Yto7e0gwGDNxDCoEPXYkluGIaSaHpppm7DMZDqT1dRlekd9RNvPOGFHYLKsoq28ntN3rKOvrJwzdjw1YZ2q7ABvv+e73LL6JXSU11KZHaArWYUTBETDsV9UJ4o5Ggc62Vo3Z8J2fGBLIk7E2pFgcC8DlIUhA65b6lXRlv6v1h/7hasD3JOM4VOqwotbS31o6fVcXGtxQksKyHsOCWuJjXqBNgUhORPQ45aqCav9gBX5An0Rl8LwOIRDo24Qc4Am32djdNyYhI4Z7np01Jnc2/XmpOMFDrO21FWpGR02Dq8z/g/JZN1nmvHXwoHesO5v/v62MXZ8rf2vYvdNK6W1z73LgxnWbMzye/cxvNL4wHD0efCcidMcJu8SFkrP0XO+0R9u6HO2145NtA/m+Pc6nABz0i5Kxx37/ocrG8uwryL0OdpSEbH8zcocXV2lhf7xDJe/XhMZGc/tslkBL5sb8P57I0y8nkrjD+Ynrao66AviIJc7/s1JhcRd2DBJpaVnLMurLU/2TJxXEbF85JQcp5ZbllZGeab/YG9kPVHP3di/TS+b53PbVo/9H4vFM4yMSTj+mosYy9+fXqCrKztmrX860+HGP8f2++ditHLP4jjQXyhtd1VNyHWzs3R1wSwHXr84wvefPfZfE1ZFQ/oKk92EYjm/MeS+9n3XxtuW+1QHpTaKHAv6PCRTZfTNHSLHC4WDM8Du3bsBmDt37oR58+bNA6Crq4uPfexjfOpTn+IjH/kI0WiUM844g8suu4wXv/jFxGKxCeservnz5495PHv2bAD27NkDwM6dO8nn81x++eWTrt/WNvaLqZqasWPB7Nmzh9bW1jEBJJTGSNzrUPdRVVU15nEkEgFKlYvHM72xkZlisu8jReTg6TV0dK28uIKWxXF2rMtS1RDhsT8P8OyjGSqyQ7zq0TtxQ8tgMsm9C04jcFxW71pPKp9lZ7KOnkiC1u2dPLlkDv3VKbCWsx9YixkOocqH8uyZ1UBnfRVYSzHisHFRE9l4hEwizsYlLdjhCrbQMfhBjubBHnzKqUlnqWEHARHuWHkhD85fwSdu/ypFyigPBxl09n1gjdgCW+Y1s2R9LxaHfqqop3PMGIQZyvCIESE/Mm0wmqQ20z/S/ehe5pAThNLXqoFxyDse1nEoK5aqI7uravnDaZfh9Q0xp3snFfkhepOVLO3YxLvu/d5+v0b2cch5Lu/7081sqJ/Hptp5PDzvVAbj5VSk+xhMVIws+5Knfk9L12bed/V7WJD3wd032FF7LMacfJG/eC4+Ez9Q+cNHGwwfRbfrshSIjFqm3XVLweCwynDf/a6BMQSuITd8HKPXg+GwLwhpKfqUhwHloaU76vFk1b6bACv8g6z+mDR3M1AMSoHV6Aqv8dVeYamD1DHjxY1fftzznvAsVVFLR9awtwdVYyxxF8oi0F8AG4LrlnKxyigsrA7Zkza0ZwyFAMDiYlleC29bFfC99S5/6bAUh79Qd7Ako6UwLheU9mMZboq1RJ3SlRlxShWcuWA4EDUQdyx5v7RoFPBN6TBLR7KvAvigwivLuKDVjgqwRgeyTB6OWUpVhMPtHrmwHYh7lrmpkFQUZpWFFENDX8Gwqd+Q8WF5dchHzwx4stvw/WddNvU7RAy8apFPTdzQlrHsSDucUx/yxqUBv9vl8sMNLr15yPvQlh0dSEA4/GOHn7OqqGWgaMYNWzkh8ds3fXT7984afXnYUcsPB7bV0ZB00VC0ppRdjv8TMuZ6HBu+GgMxB5qSIUsqLVfNCXjF/JCyyL5Vrl0QcGZ9yJ92O8xNWZ7fEmIMnFEX8p0NLhZ461KfdX0OA0XDlbMD9qQNv9/lsmsI1vYallRZPnW2z692OPxwo8tQ0dBaFjK7DDpzsLnfoSIGr1vkM7fc8vV1Hmt7DbszpROxsjrEMaVqxZhj6C9CV9aQKxqq4iGzyixPdTtkQoMDRF2ojVuCkNJraMzZt6Pump88KU25pWuwGEJ1tNSbcHumdI3n7f4v5pZESNSF+eWWl80PuWZeQJkH31jvcleby1m1AUULSQ+uWxRQHYM/7zbcutUl7xtSEcupdZZr5gWkhp+Du19e4BvrXb6/wcUaSLhQH7e8aYnPo10OP9/qUhu3vHhOyAtaQx7pdPjeBhffQnPSMr/csjsNmwcdYq7llBpLVw7+vNtlsACeW/oT5hq4clbA5kHDUz0ucc8SdaE/V5ofcSzdwyGVg+WMWsvlrT69RYdsAboLhnxgWFQe0J03/HGPS3/OEHUtngNRB1zH0plzcLCcWhPSmCw9T69dFPD7XS59ecM7VxaZVw7rT/P59CMRnup1GMhD1i89c40JyxcuKHBWA9y+wyVi4MrZAY92Ofx+p0Nz0vLy+QFNyYnv1142L+SyxS5ffyRLT85w9dyAp3sc7trjsqA8YG55qSA8GYGrZgd4Dty5yyEVgUuaQ1xn3zb//YIir10Y8FinYX2/QxBCmWfZOOCwsCLktYsC1vXBl/4SoStrCA2kvFLw25UzZMPS4/o4+BZiLiS9kO2DDiGwojrkukUBr5wfctcehx9vcqmLWxoSFj80vHJ+wLwKy5Pdhoc7HU6pDTmr3uo9qkwJfR4SkZlI4eAMYA/wr9veeZFIhMsuu4zzzz+fP/3pT6xZs4YHH3yQ+++/n1tuuYVvfetbRKNHZwRozxt72e0N2JzhO53DMGT16tXccMMNk65fX18/5vHe9fYKgmBCMDjeke5DRERE5ECqm6JUN5XeO9XOjuEXOmj++ePEwiJZkrxg7cNcuXYNGMOAreXhspVk41FiBZ9YwWfF09t47MxF5BIxCokEi7e0A6WvfB8/u3RjVGXfAMlsKTDrqSujEImMVBgawAktQ9EYRcclEgb4xGmjmTQpZm8YJDr4BFuChfRRDwEkyeIblzKbJZEaZIe7gjqepJcGPIrkCYiQo0CKLCkypEhTRpm3h8qgC8+GlBcyWAwh0FFWw+de+D6uWPcnXvjMXbj20LorM4BnA4IXnsbAt99H1g9Gqt0uG73cQIZFl/8jbnZgZL3JuITMe+QjBMDcoqXt7jSn3raWjs4u9pTVcdbWR5ndt4dl7Zvw/Cw/WHYen/rDzTTlsvzdSz6EF1rcMMQHYkFIzFr6HIe6UTeLZY0hPXpMQGPwgAdTCRbnCiTDkB7XZQCYXSyyK+IRYib9UObZA3eQk3dddpfF6UpGS8+7tRCWuh9dOLSvsiQE2qKT7MFSSgXGF+/s3Wlx+Hzv7WJ0OAlaXGN50ULLU70uOwYNUceyss4SDIcDS6pLyzWlLC+aXwo0jqXXr/CP7Q4mkffh/j2G+/ZAumjYnTXcvdPQlxs+Vw6UR+Gl8wo81e2wK+MwWDT4YSmgGQjGV1QO/9/w8+gaeNMKn0tbLSa0/GZH6Yv8t68KmJUa35r9u6jF8s5T9ncz477X418tC/irZYfenWDGL10WqQg81mUYLFgqo4ZlVZaYB51Z6MkbllTaSQsd08VSKOyVp8j2DxF5jo9cAwW4Y4dDMYCKqOXMOkvLIZyP8eaVW968dOxxL6u2fPrcfdfUoqp9568ubjmlduL1dt3CkOsWPvdNo2fWT02fpYXg2L/uAN66POCtyye/bi5psVzScuDX5v6uu0tbA/5m9djpCyoCXr3wYK7RA+3zUM7//p7PQ/t7c3bD2OWXVcF3XnDgdlw36jgvbg65uPm5r615lYb3nRqMBBvnNAS85QCv6ZfN2/82z2sMOa8R9tfF6Bn1cP3iwqTzDsVls0IumzV5O06ptZxSe/x1cSoiInKyUTg4A7S0tACwdevWCfO2bdsGQHV1NY8//jgLFizgmmuu4ZprrqFYLPLlL3+ZH/zgB9x///0873nPOyrt2bVrFwsW7BvXZvv27cC+CsLm5mYymQznnnvumPUGBgZ48MEHmTNnYvdOozU1NbFx48YJ0++55x5++9vf8t73vveI9yEiIiJysMoqXK59fzO2WAVrIU+EBIOlTMZacsTIxsbWhznWUtfZz845DVT1pceERNFikcatvRTigDEk8gXcIGQoYanuGSRTFicfj2KAQjTG3YtWc9mGR2iniTSl6sBYMWDO9iEGKQWNDuAE0MoeWminPaxmzo5eAjyW8yAxcmRIsZXZ1NCFT4QUaRIM0Xf2EjIXXEp7lyFy4UKaX7aYyAMbqPnnH/CpP32Re+efzZ2nPI+Ldj+Ks6CBYF4DVCYJq1KUff5nBwzAht79IrJ//5rSg/2MpWwrkvT+9p+I/+BunD29+HPrKf/E/2D8fV86WqD3D/808tiLGC68LAWXnTMy7TOfqCB8/CFe8GxpPMb3P/pbAG5d9QIC18UQEo0Y3nVjJV+6z1K1xcfDEg1DAmNwrKXoGKoDQ7vnUhUEBMYw3w9wsLRFPHZ5HrEw5IqBNHFrKRjodj2GPJfeyNiPZhVhSNYx9BmH6lE3+4WUvuIuGENPYl8gHCv61GYK7E7F2VoWozFXJADSBqoKPkOOs6+Sbe9ZDy34wagAMCx1NRqPsLQq4HsvtbSWW1wHhgqQGrlXcHxfnDNLzINLZlsumQ3P3a9mwP6+ZO/IwLqeUlWYcaA6ZllRM7HH0CvnHZ9fkidHXbKn140vq4T6BNQn9n9+yiKlY62OG4Kh567YqIjCaw4ihJvppiIYFBEREZETn8LBGWD58uXU1dVxyy238KpXvWpk3MGhoSF+8pOfUFdXh+M4vPWtb+V973sfb3zjG4FSNeHSpUuBfZVze/97oGrE5/KjH/2Ij370oyOPv/e97+G6LpdccglQGvfvu9/9LmvWrOGiiy4aWe7rX/86//M//8O///u/j+kidLwLL7yQBx54gD/+8Y9ceumlI9O///3vs379ev7hH/7hiPcx3tE4LyIiInJyc95yDuHNd1HZ1487KiwopwfP+vhmbEDoey4LNu+htndoZFrgWRau201zVy9rV7SwfMsuGvpK8y0hVfRQJMLamgXcfcEqqnKD1PX3kyFOmoklNh4hwajSsTRJANxCQHU+TR8N9NFAnEE6nDoqzQA5KqhzO0gvrqH9b95Mw0sWAjC6A3v//KX03/FPAJw2PG2IN0/Yf+HiZVRd+1mc4tgv/C1QOG8J2X987YFO6b7lq8rIvvOqkcf5/3Mp3gMbiH/j9wTzGsl+/FWl0OsAbnhvLd//+rn8NDvAFevvJu4XuG/ean5xyguojIacdWaMiy9NUV3j8oF4wGu3hWRtWApWh98DloeWmmKB+WHAM6kkCwo+DpAzhq0uJIKAej+kyy11G2cN+MbQUvTBQJ/r4gKDjuGh4TEJTRDSGIRUY8k7DgOOQ9HAABZbDMGxpIo+F/QOEbGW8iBgczxKRzxKXRAQAeKzovz+KktTyhLzDJ6BspGgb18nqIUABgs+NXEm9MSROjqdiMgoDUloSOrzg4iIiIiITD2FgzOA53l86EMf4mMf+xhvetObuOaaawC49dZb6erq4rOf/Synnnoqp59+Ov/1X/9FW1sbixcvpr29nR/96EfMmzdvpMKuuroagFtuuYXu7m6uuuqq/e53f375y18yNDTE6tWruffee7n77ru54YYbRsK4N7/5zdx555387d/+Lddeey0LFizg8ccf59e//jUXXHABF1xwwQG3/8pXvpLbbruNj33sY7z61a9m7ty5rFmzhgceeIB/+Id/wHXdI97HeFVVVTiOw5///Geampq47LLLqKioeO4VRUREZMawdSnSv3kPsa/ehfnW3cPj94VU0MGS7DOsLVs1ZvmL1j5O3dAA3dQBlq2z6invyTK7rYuGcJDM7shIMAhgcCiSoJF2KnsGCR6EKzvvxrOl/XgUKTJ2HOlwXN1enDyDboIOp3rU6FUB2XiEBeFmzAPvJT6rDCiNyZY8wnMSnLeM7l3fxN24B7Z1UvHhb4Fj6PveB7FLZx3+hj0X/8LlDF24/KBXqa33eM/f1VP4wGvJRF5HzjEsB/7vJMuePcflxrOKfONhQ3noUBuUzqQbhpQXC+SSHiujBbaXR0kHhtpEyFuTBV5yZoRTTo1jTAKAMLR87XtD3LcOmgs+jfjkjKEAPJyM49iQ0BjaXYd218FGXSIWqgoFXM8jCEOqCgGtuSLr41HaPMPSKsO/P89wzmyHsqhDXXL0OGQHFnWhNvHcy4mIiIiIiMiJTeHgDHH55ZdTUVHB//t//4+bb74Zz/NYtWoVn/jEJzj99NMB+Ld/+zduvvlm7r77bn72s59RXl7OZZddxjve8Q4ikdKd7Oeccw5XXHEFd911Fw899BCXXnopsVjsQLue4POf/zw333wzf/zjH5k1axYf//jHecUrXjEyv7Kykm984xt87Wtf4/e//z0DAwM0NTXxtre9jTe/+c3POf5fPB7npptu4qtf/Sq/+c1vGBoaYv78+XzmM5/h8ssvPyr7mGyf73rXu/jud7/L5z//eVpbWznrrLMOaRsiIiJy8rPzaxn86IsZ/PYuFtiNuBQxwNLcBsqDQfZEW4iHOVpyHVTaNGBZwDMYQk7fE2GbM5e0rSdKjgWdHZTqwvbJD4d/cQos7Ng9HECWNLCTXSxgb0jkRwx9XpKybGnsoFzc494FS0l15lk58CxVFf30kiSXiBAsbsL+x+tJDAeDR1uwqBkWNdP7yBePyfYPRTR2cO8D/+aFHivDn/OaV7+J7bt72LUDknHD3LmVRGMHF7A5juFdbyrnNT0B3/55lqc2+QQWItZySr5A0XNY0OzwgmUOK1shZou0NHqUp0a//3Zg3HUgIiIiIiIiciDGqh9EmSI33XQTN998M7fddtvIOIhy7HR2Dk53E0SOOWOgrq6crq7B5xynRkQm0mtoevT/YhNDb/0TkbJ+knaQ+ZltY+b7uDjDIZ/BxzB27LwB6kkxRA91tDO2ui5BhibaANhmWlhinwVCXPIYLDkS9FMDuPxu9Rk8Om85je19ALQ3VuEVA150z32cmtlCuPMTx+gMnBx83+eOO37OW97yFtraenFd3Xcpcqj075DIkdFrSOTI6DUkU6W+vny6m3DSypj3HpXtJO2Xj8p2TiSHVh4lIiIiIiJHJLW6lp5UkofrV/JA7WqyTnzM/LQ7umvy8WPxuaQodSVaRTdJ9t0M5OJTQzcARTx21ZW6g3coYih92xEnSyO7cJwM7oAldBz2NNewp7mG0HGY07OH8/oex3/TOUf7sEVERERERETkOKHbW+WIdHV1HdRyyeSRjkgjIiIicnLIZBz6UqVAMO/Guaf6XGZn9xAPsuyOzSJqA04ffHp4aQOMvo3ZGfWbZS6bSZMkTTkhLj4OQ1Swrm4eO5qa+EvmVBrTHdTSRoLMyLoRN8+le9awcd5suqsqAWjo7+aVf/kT6YtPw/nkFcf4LIiIiIiIiIjIdFE4KEfkqquuOqjlbrjhhmPcEhEREZETQ6w+RtxmGaR085R1YVt8LgPecMWgtczPbKMyGMLiYfBxRgLCgPFv4Ytxh1yuHIOhSGlMwLndPczqKlUV9lPHADXMZ+1IQPjwnFWU5wq8Y8ufaP+nVxAuq6Vp3myMOR31qCQiIiIiIiInAsvBjfcuEykclCPyla985aCWmzVrFq2trbz97W8/xi0SEREROb5FE4ZTBjbwh8T5GKAtUc9Ld/6JrmgtGSdBU6ED48EObxb5RITfrLiAt9//PWJhgfZIHaFJ0FQo9d6QNxGSuTyD4z4QhdYd89ji0Es9CbYR4PC7FRcRLfq8deApGl/UOlWHLiIiIiIiIiLHAYWDckTOPffc6W6CiIiIyAml8OAOdtc2s3NeCxW9A2AM32u+htc+fjvNuXZ+fMbVrG+aD8Di3Tu56IkNPNRyJqs6n+Sx1Goybhnl/iDR0KcnUsn5fY9C0cKogHCy6r8icQokeaRuKUUvQtGLsLF5AadNzWGLiIiIiIiIyHFC4aCIiIiIyBQqNlbzWOsqsmUJsmWJkem3rbqc2qE+1jcvGJn27KzZVA9keemzvwDjk3FL3YYOeuUjywx6ZTQWO+mjnr0BYZQCeeKj9mopI80OZvPnxecAkMpkyQzmjt2BioiIiIiIiMhxSeGgiIiIiMgUSgzliAT+hOmRoEhnpIrTHt2I5wd011WwbV4ju+pq4FmI2iLl/gCDe8cmHFZb7KOCPuIMMkQdAQ5d1JKNe/jGJRL6xIt5doSzaa+toKOpllihwJJtu/CuVJeiIiIiIiIiIjONM90NEBERERGZScyyOk7fsgFj93X+GctlCdKW2t1DJLIFIsWApj29zNnaQaxQpJ96AM4aeJh4kC1tx4YsTm+m2u8j48QY8MoZcGOsrVnAswsa2DSvkVnZLmryg0TDAFsWsnlOA7PaOqgeHMKxltP+ZvG0nAMRERERERGRI2eO0s/Mo8pBEREREZGplIxQ0ZPl+fc9webZTYSRgDBviWTCCYvWdfaxc34tPzrzCpZ1PsPzd9zPi7p/w47ELHwnTnuslu50DXkT4ZkXXsbyL57FqpoYzsNb4c3fYkdDFQNeJYMmxc7aBvLxKJ611MZDzvvO+UTjuldQREREREREZKZROCgiIiIiMoXSOzJ0VpUzu6eNmt40D502Bye0hM7EuxVdG+IElsA4JIs+ro0AIYPRWup//0aqikUKDVWYfMBp9fvGGAzPmgdP/ROzgFnD086ZioMTERERERERkeOewkERERERkSnkJBxmFXZTwyCbWURgHBwCsgmPRNbHDUvdjVqgvb6KRDbLKV3PcF7bXwDLkBPHfuGluHOrsEAERv5fREREREREROS5KBwUEREREZlCQRHKggIJcizmGbb1VLEzVUvoOvTUxElkfWKFgOpMlr+01FDT289QrJzNNfMIzp5P+b+/hOba5HQfhoiIiIiIiIicoBQOioiIiIhMpVye9fXzWLF7K/eXn46f9ojGoRA1hDi4xjK/vQfXWk5/aitn/Pr5JGadP92tFhEREREREZGThMJBEREREZEp5D3Tya9PvYigL47vlN6OL9nSRnkxT4g7ZtnGrn7CvD8dzRQRERERERE5rlnMdDfhhOVMdwNERERERGaS3roaoqFP2k0AEC0UWVTcTi3dE5bNRxx61w9OdRNFRERERERE5CSmcFBEREREZApVLa/ihevuodrvBWBudg8VDFFFP0kyI8v5xmBTRVKn1ExXU0VERERERETkJKRwUERERERkCrntGZa2bcYdjFDXnabKTwPgYJnNTuawjSb2ELN5fC9K1ezENLdYRERERERERE4mCgdFRERERKaQE7VkspUERDBAgciY+QnyGBMSAVp392GzGnNQRERERERERI4eb7obICIiIiIyk8S37KGT6pHHBSdKGBocLAXHY13dbHan6qjsK9Dc00/QlsabXzmNLRYRERERERE5/ljMdDfhhKVwUERERERkCvkrW4mSH3kcRGAonyJKjt8uPJPeRDkAHSkYLItycWy6WioiIiIiIiIiJyN1KyoiIiIiMoXykSi9jQkwIXEKhPk43dSxNrVoJBjcq62qktye9DS1VERERERERERORgoHRURERESmUDYdsGbhaqjJEKc40gmK77gTFzYGv7l64nQRERERERERkcOkcFBEREREZApFEy6h59JdVjtmes1ghkjRHzPNGkOqJTGVzRMRERERERGRk5zCQRERERGRKVRWHaHptEp21tWPme5ay6rNu4n6PoHjkI9FOfv8+DS1UkREREREREROVgoHRURERESm2Is+Np8Fb1nIjmVNWCwAFtiwcBbtzQ2kq8pZMdcw/5NnT29DRUREREREROSk4013A0REREREZhrHNZx1XRNcdzVBW4Zgcz/u8mqueLqdvmSKiqVVxMomGYNQREREREREROQIKRwUEREREZlGblMStylZ+v2iudQ/x/IiIiIiIiIiIkdC3YqKiIiIiIiIiIiIiIiIzBAKB0VERERERERERERERERmCHUrKiIiIiIiIiIiIiIiIicUi5nuJpywVDkoIiIiIiIiIiIiIiIiMkMoHBQRERERERERERERERGZIRQOioiIiIiIiIiIiIiIiMwQCgdFREREREREREREREREZgiFgyIiIiIiIiIiIiIiIiIzhDfdDRARERERERERERERERE5NGa6G3DCUuWgiIiIiIiIiIiIiIiIyAyhcFBERERERERERERERERkhlA4KCIiIiIiIiIiIiIiIjJDKBwUERERERERERERERERmSG86W6AiIiIiIiIiIiIiIiIyKGwmOluwglLlYMiIiIiIiIiIiIiIiIiM4TCQREREREREREREREREZEZQuGgiIiIiIiIiIiIiIiIyAyhMQdFRERERGTahHvSOFu6MFEHe2YrGI0ZISIiIiIiInIsKRwUEREREZEp52/up3DF94kP9uPjkCVOck4E9/Y3Qn1qupsnIiIiIiIictJSt6IiIiIiIjLlghf9D9WD7STIUUaGWnpIb/cJ33/HdDdNRERERERETgAWc1R+ZiKFgyIiIiIiMqWsH1Le2zFmmgGq6SVzZ/v0NEpGtKXhj9sNOwanuyUiIiIiIiJyLKhbURERERERmVLGc3AIsePuVfQoYoNwmlolAP/3AcNdtw+ytG+IoYjLUy3V3PxGj1PqprtlIiIiIiIicrSoclBERERERKZWwcelMMkMg4c/5c2RknXdsO6nXVzU1kN9rsD8wSxXP7Ob13wvpC093a0TERERERGRo0WVgyIiIiIiMrWiHhY7yQyHOEqhpsuP/mKZPZTldy317CxPMBRxWTCQ4bJtndz8lyY+ccFkz5mIiIiIiIicaBQOioiIiIjIlCsysRsTCxjUreh0eXJTkccXtTIY9cAYADZWpeiJR1j5SA4uiE1zC0VERERERORoULeiIiIiIiIy5QappkBkzLQMZWQoByDcM0Th9q2Eu4emo3kzUmckOiYY3KsnHqOYC6apVSIiIiIiIvtjjtLPzKPKQRERERERmXIRAoo4FIkDDiGGNBUYfPINN41a0uBeNYeK71w1XU2dMfoKzoRgcK+eVHyKWyMiIiIiIiLHiioHRURERERkysXJkaaKQWoYpIoMKTxyWCKMv4MzuGMbvSu/jbUa8+5Yyhzgjtl+Vx8dRUREREREThb6hCciIiIiIlMuTQUh7shji3uADycGOvNk/vWhqWjajOXuLxs0kHHc/cwUERERERGRE43CQRERERERmXL+uPEGgTFh4WQK//X4MWqNAMSK/qTTG3M5YsoGRUREREREThoac1BERERERKaUHSpimNhFaPhcA8H74TFqkewchK7AZbJ8thgaFsV8eK7nR0REREREZApp4InDp8pBERERERGZWp4hRxKPPIYQCHHwcQmmu2Uz1jt+57KoPz3pvJ5EjPrB3BS3SERERERERI4VhYMiIiIiIjKlTNwDQkJcLA5giFJ4zm5F5djZ1GG5eE83TjixOtOxlnRBVYMiIiIiIiInC4WDIiIiIiIyNYoB3hu/T6Txk9TQTZQsLnkcAhzsc3QrasHo48uxUMiHrN7WRTIIedmW3TSmx1YJNqdzlDvq0lVERERERORkoTEHRURERERkSrjv/ClDv2knRwMOIQEOe+9XzFCqJtw/g/HU7eix8Nf/leb+uXWs7B1ibjpPy+bdrK0ppzsWpTWdpS5boGtVzXQ3U0RERERERI4ShYMiIiIiIjIl0r/cRZYygOFgcLwDVwY6RtVrR1tvHm6rqgMMBQPGGKKh5bTugZFlMo5hXVYfHUVERERERE4W+oQnIiIiIiJTIhfGj2j9wImMndCTgQ1dhLMqcWZXHtG2Z6q7djmEjkM87/Pj5XPIJSIUXYflnf1cvLUTz1p2pJJ4Gg5SRERERESOM/aAQ1PIgSgcFBERERGRKWEciz2S4r/C8H/bBzFv+jFDjw1SIAEYjBNim5N4FTGSX74E97Smo9Dik9/SqhCsJWcMucrEyPQnmqqJ+SEX7Oji8bpKTp3nAXb6GioiIiIiIiJHzYH77RERERERETlKjGcZGzDZUT8HIQyhO01x9dfoeaw4EgwC2NCBXVn8df0MXHEr2Q/87ug2/jgWBJZ7fzPA7d/vZqDXP6R1K2NAIQRn4h23G2tT7E7GaCuL89uOCI916q5cERERERGRk4EqB0VERERE5JgLt/fhFAoExIC9fVQeatgUknzBZ2gLmpj8Pkcz8t/c/2ymsO02Kn7wYkzs5O0Tc2jA5z8+vBtL6egfuSvNFa+q5NzL99/NaroI6zstgYUb74xAEOJiCLyx5zTAcOv8ZogaKFqu/GWUaCFgUbTIZ0/P09kZsGJ5jAWt+lgpIiIiIiJyItGnOBEREREROaZiN3yb+K2PkyJKL41kSB3WdlL0kt6dJGDc2IMj0dhohnDNTrL/90GS/3T+Ye3vRHDTP+0GRsei8Ov/HdhvOPjhOy2dv+1mQV+GojHUN9Wwu7GKIAT8EIYDwpXtfSzuHCDnuTzWUk17JApFS2M6yynruvnSlgT3zakju9tjQZjjp6+xtJQd++MVERERERGRI6dwUEREREREjhnvt0+SvPVRDIYiLoc7bp1DgMHBHrBicKwCUex/PUTuq09iIg7Oq5eQfPkCvOfNwpiTo4vMdGZfHeZeEWuxFsYf4m82Qe8dXSwayALwdH0Ff2mogr3jQOZDCC1ndvRx0Y6ukfUqikV+smw2NoCdZUk21+SZN5ApzbSWzSbGpd8v8tsrssydFz0mxykiIiIiIjLRyfG5bjpozEERERERETlmEl/4DWAoEqVAlAyHV14W4lKgjBg5zEiadWAeIXnKMRYo+CT+50GSr/5/RBs/SfDlBwCw+eCw2nO82F/r/7Bz4ofk//u7IumoxyPN1exKxbh3Vt3ErNa3LOvspzMeHdn2rxa0YAuWSCHgRZvbOG93D01DOa55egcm7UMmoDdwuPgWj/TQiX0+RUREREREZgJVDoqIiIiIyDFj9gxRJEaRBD4xDJYIOXwihBNq3g4sT5wQh0o66aOBsXeJ+oR4Y+5+dIAI/sijNBUYIMUQiU/dTuZL9xBLZ7BYgtZyuOudmFTsiI73eBFiGCxY3vT7CPe2OUQLRSryHg2uy5l7emkvixE4BoJx6aCFHy1qxXccyoo+l+3qJG0ccAwVNmRDYyWhMeQtlPkBK3sGeGpWNVjIJaIs+V7IPa8Lmbf/IQ9FRERERERkmikcFBERERGRY6aQrCSkFON5FGigHQPkidJL3SFvL8CliIdHEQskyeLi4+HTS82E8Qjdkb2XDFFOhjKi5KlK941Mj+zsI1j6Kfxd/3I4hzltxo++uNfzWkLO/nGUjq4AWyhig5DZ/YOc0t0PQFW2wLl7eshjWF9VTs7bF9T6TiliTUc8fjerHhwDcZduXLqjETaO2mt1oVgKGKMulBmKxZDLb7VsfFPxmB2ziIiIiIiIHBmFgyIiIiIickx4T2wl0ruHAnHAwR3Th+Whjw3hUaCfakJcHALq6MQZ1cVojBwZvHHbNuzrO9MAhhCXHAnaiROhSDn9RPApFGOkG/4N21pH2S+uxZ1VfshtPF48vrVId7eDLVpW9Q/xvM7e0liEw/NjoeW83T0AnNHZxw8Xt5KJeozvsTUX8TCe2ffMpf0x83ujEcgF4FuIlCoMB3yN+yEiIiIiInI805iDIiIiIiJy1EXuepqqy/+ReG87HpkJ86PkcfY7Yt5YhpAEaeJkR7oiTZAZEwwCVDBIPe14jK9amyysMlgcCsTopRYLxAjYYxpp223JnnvzQbXteGSBT38ny9zuIcp8n+d39BCxpXhvsjNRXgy4buNOXvXMzufYsIVw/CCFlKZZoBCWfrIBy78T4debj/RIRERERERE5FhQOCgiIiIiIkdd8u1fxwAhLj5lE+YboJpuSqVqIRBg8Ccst1c5A+PqAScJqSh1I1rOwCRzJq9mi5CnilIFXYhhsd3JEJV0+WUET3aMLBcMFXn2r+7nibNu55nr7ib7zGT7mDq5TMCX/3H3+EI/8q6DA6zqGWR2UGTRUOagRnasKASU+UEpABynqW843DWmVB04njtqmi0t152GN/8uwqKvGp7tOciDEhEREREROQQWc1R+ZiJ1KyoiIiIiIkeV8+uncLoHsXj4JJgsmLNAgRgNtOMMB31FPLqpY/w9jBaHQSpIMYRDhiraMIQEJChSOWH7UfLDezjwhzxDSDU9I/v3CHHJk6RALzVE3/Y7mte8jm3/8Be6v7V1ZL3irhwbLv8zVasgd1WG9CPddNy0mcKOLKlzapj1sVW4ZUf/o1a6CF05Q23M8vm/2UVPWZxHF9dTcB2Wdg6wpGeQWFCKC8uKASs7B3ikuXrCmZjszPRHPH60uLUUAI5mLd3WgcECxF2IOuCH+3pq9UpdiY5eHmNK8y0M+IbLfmD4xkssV8ydPNAVERERERGRqaVwUEREREREDlvkzieJ3rsOf3ELhYtXEH3elzADFkuKEIuZpLMSC/RTQZIsIQ45onj4RCmSIEt2kkrDLElcMrSwcdTUHBYXn7FjAxrAxScgcsC2x8iNBINj1g0LRBxDx7Y4Ha23EDiTbyfxlMufZt8ytp1P9dP5jS0QMcQXlRP0F7CpKF2nNTLYmAKgc3uO0Id182vZ6CWpHcqzIjOEn4yQzPvk8z57TISBSIRHG6o5raePlZ0DGGvZXFVOWTrPgp3t/Pe1FxMMV+2lox7LuwfHtMWzlv54BAN0xKN0JmI0ZHMk/bBUJTgsAH6wuJW8N0mNoYWC4+zrMnTY4kyaZxuroDjq/FkLhaAUFkbcUkFo1CPvwfV/MJhiSFkYcFqzoS1jmFVmWVZjaSmHVy8JqU8e8OkSERERERGRo0ThoJyU7rnnHv77v/+bTZs2UVtby/XXX88zzzzDgw8+yC9+8QtuvPFGYrEYy5cv5wc/+AHxeJyvfvWrfO5zn2PPnj384he/GLO9G2+8cWT67t27ednLXrbffd9www28/e1vP9aHKCIiIjLtyv71JyT/45cjj4PKFOmBWiKjQjeHEI88PrGR5QxQyQAZkgyOqvyLkyEyYbzAfWuV0z1haoxefFKMr4VLkGaIqgO2P5ykw00LZJwEvvH2NfZwFC25dXu7Hs1RuWGAttNn019bCj6/t2weG6srSrMr4JFMFa/etJPdrmF+f44KcgCc0d4zppUru/r4+qqFPDGvYSQYBBiMRggMuOOK87pdj9/PquPp2sqRaQ35PK8bNb5gWyJKPhWFfADji/v2c/xb6ytLVYN+AMXh0DAYrigMbKkhjin9HnHAdbCuw1AG7tldWnxTn+GuXaXf/+txh9++yqcldYBzKiIiIiIiIkeFwkE56dx999186EMfYuHChbz73e+mo6ODf//3fyeRSJBM7rsd+fHHH2fnzp28733vY9euXcyfP/+gtl9dXc0nP/nJCdNvuukm2tvbueCCC47asRyp8b1CiZxs9l7jutZFDo9eQ3IkTH+axNd+M25aFnBwCcYtPX5kPADDEBWMTp9yJAmGQ7HJOr+cPMyLEOKMqQC0QITC8H7HVy7u226BKAWiRCmMzO2nYl8weBQZoHF7D/21ZbTFY2ytHJuCdSRjbKwoY2l335jp44/YtZaLd3bwTEvtmOn5iMvDzTWcu3vfAH+P1VbQkYjTkSgFs8ZamjM58o7DnmSM5kwegDDigmtKXYbmxz1XxgyHgKOmxxyKGMgOj1HoT/L8hnZfd6PB8IFYOzF83Hv8GcM3n3L4+/Mnu1bkZKV/h0SOjF5DIkdGryERmckUDspJ5wtf+AKzZs3iG9/4BvF4HIDTTjuND33oQ2PCwWw2y7/8y7+watWqQ9p+IpHg6quvHjPtO9/5Drt27eLDH/4wp5xyypEfxFFQXZ3Em6xrKJGTUG1t+XMvJCL7pdeQHJaBNOTHV/mVBpoL8HBGBW52kq5FA5xJpxdHKgwnfkvTRzPldOEMh40WaGMBOSqooI8YeUIchqjA7ieFKuLQR5Ja0jhAD7UkyODhkyfKVqfpIA7+8DhhqU0dqTi+M/HYByMRXPvc4/K51rKio58nGqsIhwO4qB8QDwLunFWHsdCZiLKnqgwiBoyhaijPy5/ZSWXBBxgT31bki6Xgbu/4gUGpDWW5IhljsOWRUtjnh+A6pUrA0JbCwf0Z/fRNPNRJ9QUR6uqiB7ewnFT075DIkdFrSOTI6DUkcuKyh93ViygclJPKs88+y86dO3n/+98/EgwCPP/5z2fevHnk8/mRabFYjBUrVhzxPu+77z6+8pWvcPXVV/Oa17zmiLd3tPT2ZnTnk5z0jCm9ie/uHuQgvksVkXH0GpIjUlFG1crZeE/vGJlkhsPBInEcgpEKwtL4f7ExqxtCJqsOPFA/nnlS7GIFKbpx8emjmdzweIN91AIhMXK4BORIEiFLkeSYbWaJEhqHgvWI4w9PKyME9pgq8ubA4xQeia6WUteeC/qGiPnBmDH+HGtZMDDEM9UVnNO+r/vU8Wco5zr8bk4Tr9u0m1eu3cGjLdUMRjxeuKmNumyB9VUpfjOncV8l4LCLd3aOBIMwtiKxMu+zqr2PLdUpTt/Ty/2z6/Bdh3Rk1HPmDFcQ7rW3UcaUwsLiqIo/h31VgwC+LX3yNKa03n7+3rygpUBXV37ymXJS0r9DIkdGryGRI6PXkEyVujoF0HL8UTgoJ5Xt27cDMGfOnAnz5s2bxzPPPDPyuKqqCmeSO7YPdX8f+9jHWLhwIR/72MeOaFvHgt7YyExhra53kSOh15Acrv5vvIfyv/02kXvWESxpIfvq5+F88i5CPHKkcCgSI4NHkYACARHAEGDopp59SZEZCQvtJF2H7lVBDyn6SFM5vI/xH7Id8iSpoI8WNpKkiwIJBqkjR4IuWokRUGEHJ2zbAZIUyDMuHLT2iPqaskAhHmH3vBq6miswBlJ+wAu27eGu2Y2kIx7lBZ/zt7dTXvAJjeHPsxpY2jtA1nPZVFXB2R29YEO2VCRZM6uBwUiErGNoGcyS2B7wx3kN1GVLlZqL+4bYWFnGpvqKMe1oHMpN0rp9ztzZw+o9fTzRXIXvjnuPPNk5GN2VqDuq4tAMPx4tGK469C2ElohbyhJjbmnTlTH469NDrppv9bdohtK/QyJHRq8hkSOj15CIzEQKB+Wk4vulu6Gj0YndEY2fdijBYBhOHPsknU7zoQ99CGMMn/vc58ZUKoqIiIjMBOH8Rvpv+fDYia85j9iV/427q4OAJIbSWIF7g8EQ6KJxVJeipYCwhk4MId1j5o1VQS8uBVL0YYE0ScLxYR6QJUEDPRggRpY8OXYznxBDAn/C8iU+1TZDMiiQNRECDP0mSe0ZHt1PBky2Wuhaqs5vYGBtD/QFpSEOI4bqq5qZ+29n4FbsvwrxXUA+gN1DPrPLwXMaR+ZZCw/sqWVtj8O7Z4c0uBXccl+BNDFeW+6y6ffdJMJSBzpr6ysoG1cR+JJt7fzZBjw+a9+4hG2pBAt7h/bbnq3JOKv7h+j3PMj6pWNxDY2FPGft7iVZ9NlYW87jzdVgS2FjW3RUZaEx4JlSyjr+rbO1lJmQG0/3+eg5+uZNRERERERkuikclJPKrFmzANi2bRvnnXfemHk7duyYbJUxXNelUChMmN7d3T3msbWWT3ziE2zZsoUvfelLtLa2HkGrRURERE4iDZXkH/tbkg0fpFQlGMEfDgYBAiKThH+GHEkylO03GHQIcCjiMUiM0liH8+lkO6spMvYmLUM4Mi5hlnLaWDzchgMp7TeGT8z69BJn6e2XkDqjlrmhZcOr1jB0X9feHdD6uVN5qOoxrn7Li2hr68V1D/2jVcyF+ZUTpxsD57XAeS17UzaHN12x9xhD/DOr+NB/GOz2LBZDwZt4zs7f2c1TDVX4kVIl5po5ddSmc1QNB4kh+4YCHPJcej2PjOuwKxYbCfcqMwWu3bCTyPCt9C1DORLFgHvn1tNWXQYZf2wXodZCOLHK8jPPC/irUxQKioiIiIiIHC8UDspJZcWKFTQ2NnLrrbfyile8YqRa8Mknn2T9+vU0NTUdcP3a2loeeeQROjs7qa+vB2DdunXs2LGD5ubmkeW+9rWvcdddd/H2t7+diy666NgdkIiIiMgJKv+6i/F+8Bh5kpjhsQcBPHwM4bgQ0JIlsd9gsLRegQBLfDgYBHAJqGUrbSwbs2yRGH00E2OQdhYCBgdLOBwxehNK26AUle0d4c/S9KI6kmeUKu+MY1j604sJhorYfIhXGyv1WHHHY4d8Xo4GL2L49w9VkU1X8LG/7+DW5bPwGfvhzgst5X05esvjYMDJ+vyhpY7Acch7LlnPZf5Amupcgbpcnp21KQqJCP6o3jWW9w6OBIN7rero4965pffJxF3K+3MMRjyiYUhTOsf2irLSvKhDLAz4wdU+F+k+OhERERERkeOKwkE5qTiOwwc+8AE++tGP8ld/9Ve8+MUvpre3lx/+8IdEo1HMc4wXc+WVV3LHHXfw3ve+l2uvvZaenh5+9KMfMWfOHIrF0hdRa9as4Rvf+Abz589n4cKF3HHHHWO6Ha2pqZlQtSgiIiIy0wT/8QrC+hSRL99DQBR3OJAzWCrpo49q9gZx5QwwyCQldABYDCEFEkTITpgbI4tDEYs7Ei5aHDqYP7yEGV6uSJYoOaLEKOINB5aTvjucXU7y21dPmOymIpA6+HNwrKVdh9psgas37GFtYyUrOgbwrMUC7TUxGnI5GvJ5ksWAnniUbeVJMIYF/WmaMjkqCkUebqwm75aqC3tiY7vhn6QIkJgf8raHN7Kxppw1c+u5emcHDUM5HOD7i/amgJY7rilwRr2qBUVERERERI5HCgflpHP55ZcD8PWvf50vf/nLNDQ08IEPfIBf/epX9Pb2HnDdiy++mI985CP84Ac/4Atf+AJz5szhox/9KI888ghr1qwB4Omnn8Zay5YtW/jwhz88YRtnnHGGwkERERERwP79FfDfD1DMlSryXCxFPPLE2BvLufhEyeGRwGfiuNGlOLEUXoWTRHlZKoiTJzMhtRu7rEtIGTl8XMxwX5gTtxZQ9qOXEr109qEe6vQYzt6ah3I0D+XGzPqHjzdy6Xcg2V/gmeqK0sRiAJ7D5ookK3oHWVddPhIMAqX+TEdZV13OGZ19xMJ9IZ8DlBUDTmvvI1H0aRzKUXQM9zfW0JksjUFYGQ0VDIqIiIiIiBzHjLVWn9rkpBEEAQMDA1RXV0+Yd91111FRUcHNN988DS2bep2dg9PdBJFjzhioqyunq2sQ/Wsmcuj0GpKpEH3xTTgP7WSQCopEKeISjhqDME6GKvoo4tFNHRyga9Fm1uFSJEnpfU6GCnazjARZMqQO2C1pyd5uQ8EO/8/ZW21oLNW3XYk5d95BHZfv+9xxx895y1vecthjDh6pXAD/9u7tE0JOC7z/P+ewtsdw/S9cGCoyEIlg86VwkEnGKNxr7kCa9mScnOeCtdQOZDmjd4CFgxli4/5Q5B2H/1nSStZzS92RWgvGcO/riiya+HZcZAL9OyRyZPQaEjkyeg3JVKmvL5/uJpy0uszHj8p26uy/HpXtnEhUOSgnlTAMedGLXsTLXvYyPvaxj41M37hxI5s3b+b666+fxtaJiIiIzDzmgtnYh/aQIo0lDYCPSzcNlMYCLHU3GsGnnjb6qKZIfO/aY7bVwyzyVOCRw2ApkgAgh6GMQYaoYF9FYoFgQiWiGfWbIfaWFXir6sCxRF+7DOM+V7h4fIm7+5+XisA5jZZn3+azJ23YM1jkpT9yKPohBCHEvdLpGDf84pL+NKd1D3Db/NJ4293JGPe7VSwfSE/YR9ExDEYjpQfDweA3np9nUfWJdR5FRERERERmGoWDclKJRCJcccUV3HrrrRhjWL58OV1dXfzkJz+hqqqKN7zhDdPdRBEREZEZxb9iOZH/uB+LMxLNRQho4Rl2s4QsScpI4xKQpnwk8JtMjgoMIf5IeFgSDI8g2MA20tRQJEaBCEkGSZPaFwk6BpIe1Cco+9cLiV4+51gc8pSabMzE8dOay6C5zPDF5/u85w9eqbTQASIuXqZYqvoD5g2kWdo7iAN4YViaHnFJFIuT7mdjZdnI707E8LPT+zl/RfLoHJiIiIiIiIgcMwoH5aTz8Y9/nLlz5/LrX/+aX/7yl6RSKc455xze+c53UldXN93NExEREZlRgnPmE0+F+EOGvbGVxxAJuqmkE58kBSJYPDKUHXBbpU5Bx1alRUgzi3VEyTNAHWkqRuZ5FIlcPZ+yLz4PO1jAnVvBycY4YMPnXg7gutNcbr0nwx+CFNZacA0LclmWt/WTDAIasgUAcq5DMGr8wY5EjL6oR1XBH5mWdxzWNNUAEI3Cg68r0lKmYFBERERERKaOnfQ2RjkYCgflpBOPx3nb297G2972tuluioiIiIgA6Y2fJPbyr2Lv30UftRRpwLKAGrpJDI8f2E0tBxpvMARc8riuwQ88HELiZCmnl34ayFJFblQw6BCQI0nZpy7AqYlDTXy/2z6Rvfqddfz4K11jptXP2n9/o99/V4JHHxrgD48UedCUs6YmxTm7eqgdDgYBHq6vwo4KBzGG2+Y1c+muTpozeXpjEdbMraM65XDjaT7vP0OD9IiIiIiIiJxIFA6KiIiIiMix5Tjkb3s3+YbPExAnxMPFp0gUjyxFIhSJHXgTQJwCqW9eTt+b/oxPFJ8onbQQjnys2VtbGOCRIfLVl+K0po710U2rxackue499fzqO10U8pbVF6a44tU1B1znjLPLOOPs0u+7erNcMtjAgnSOirzP1vIk28sTEIxdpzce5YFFDdz9+oBU5d7w0UdEREREREROPAoHRURERERkShRw8YkApXECB6gmQxkOB9cvpsXFv+o0qr/lU3jr7eSCOFEMARFc8tSyA4ciNFWSvu1D2Hkzo0v5RSsTvO+zsw9r3d60IZuK83hVGYQW8sGEYBAgYkN++FpGBYMiIiIiIiJyolI4KCIiIiIiUyLu5hgKJlbyFTi4Lj8LxEgAxavPxOw5k+STO3G/eCeO5+BfuoLs8+djGyrAU4B1sCoqHKJBSNFzIR8yNqe1tKazdMeiVLsBS2v23+2riIiIiIiInDgUDoqIiIiIyNSoTELPvocu/kgl4VghYDBY7KhxCMNxg83bU1rxv/mmY9PWGSITGOb3DvFUY1WpcnAMg7WGrOdRNA6TlhSKiIiIiIjICUe3foqIiIiIyJQI33URUTIjj12CSbsULaeXBTzEYu5jFk/hkR+eMz68kiO1rNqSdxwihYBx2SsAu8pKVZ1RfXIUEREREZHjjMUclZ+ZSB/xRERERERkSkTffQa2IolLHrAUiJBkaMwyDgF1bMKjCEAZ/TSycRpaO3NcdEGCikyBqsDnFZt389dPbuK1z+6gJZ0FU/qgvLiYneZWioiIiIiIyNGicFBERERERKaEcR3K1r+N6KefTwPbqKQdlxxNrKeSNqrZSTPPEhkOBvcqow8zSYWhHB2fvTDAM5aXbNrDnKEsroXGbIGXbWkjGpS6Em0cyE1zK0VERERERORoUTgoIiIiIiJTxngO8bedQp4U9WymgY1U0E0jm6hnGwl6J6zjExnu6kUfX46VOdkctdnCmGmxMGTOYKlicFG0ONlqIiIiIiIicgLSp2sREREREZlyGZIUKJ8wuoNDiB31McUCXcylNCCexhw8Vm5cHY6pzcy4Dh2JKOmIC9bSXRGftraJiIiIiIjI0eVNdwNERERERGTmiVGkj1k0sX7MdAsUqKSHOiLkGaKGAmXDc9W16LFyzVUpfvPHNLP689zXWM3D9dWEjil1K2oMd/llz70RERERERGRKTX+dlM5WAoHRURERERkSoW7BjFYhqiinwYq6RiZN0AjcUIGaZiwnjdXAdWx1L6wGnddLw821oxMK7guxlqKEQeFsyIiIiIiIicHhYMiIiIiIjKlbCZPnjgePu0sYoB6YqTJUUGBBHW0kWSQDOWj1yLxg5dNW5tngp4BC2UTuw+1xtBaoS5dRUREREREThYac1BERERERKaUqUjiEBInjcGSpYo+ZlEgSYhDB7PwcYmQByy4UPaTF+Mtqp7upp/U9qTiVOaLE2dYi+epux4REREREZGThSoHRURERERkSpn6BB4ZEhQIGSRHApeAcvoZogzvgbeS+/A9WD8g8bJFxF6/FBNzp7vZJz3XgUfrq1jQn2Zz5XAXrtaCMRRDhYMiIiIiIiInC4WDIiIiIiIypYxjCIgzSJwMKQB8wCdCkj68+VVEfvLi6W3kDBQpBOxOJUoPhkNBTCkU7MlNY8NERERERETkqFK3oiIiIiIiMuVyxMhQqk4zhBhCAjysPqJMmzNqw30PzNhKwRAREREREZHjiz1KPzORPnmLiIiIiMiUM8Mfwyroo4E2Gmijih7yxKa7aTPWjc/z8IJg0nnvOFXxoIiIiIiIyMlC4aCIiIiIiEy5FINUMECSDAYwQJwcZWSnu2kz1nmz4LJ54AZBqVvRYXMrLG9fPVPvpxURERERETn5aMxBERERERGZcnGvgPEnBk4ePpPXrslU+N5LQ36z1fA/6yHjW244NeDKOQoGRURERERETiYKB0VEREREZOotrCV4pgdv3Gh2tioxTQ2Sva6cZ7lynj/dzRAREREREZFjRN2KioiIiIjIlAs+9HwMISFmZFqIwf7v9dPYKhERERERETlRWMxR+ZmJVDkoIiIiIiJTLrxmFba1CufHjxM+sQfObCb4m8ugOjndTRMRERERERE5qSkcFBERERGRaWHPbCU4s3W6myEiIiIiIiIyo6hbUREREREREREREREREZEZQuGgiIiIiIiIiIiIiIiIyAyhcFBERERERERERERERERkhtCYgyIiIiIiIiIiIiIiInJCsZjpbsIJS5WDIiIiIiIiIiIiIiIiIjOEwkERERERERERERERERGRGULhoIiIiIiIiIiIiIiIiMgMoXBQREREREREREREREREZIZQOCgiIiIiIiIiIiIiIiIyQ3jT3QARERERERERERERERGRQ2OmuwEnLFUOioiIiIiIiIiIiIiIiMwQCgdFREREREREREREREREZgiFgyIiIiIiIiIiIiIiIiIzhMJBERERERERERERERERkRnCm+4GiIiIiIiIiIiIiIiIiBwKi5nuJpywVDkoIiIiIiIiIiIiIiIiMkMoHBQRERERERERERERERGZIRQOioiIiIiIiIiIiIiIiMwQCgdFREREROS44mzYTfUp76Ou8f9QveI9uOt2THeTRERERERERE4aCgdFREREROS44T34LDUXfRSvvQ9jwesaoPqSv4eh3HQ3TUREREREROSkoHBQRERERESOG5XXfR4zbpoBKl/wD9PRHBERERERETlO2aP0MxMpHBQRERERkeOGSecnne5taZ/iloiIiIiIiIicnBQOioiIiIjIcW98NaGIiIiIiIiIHB6FgyIiIiIictybqV29iIiIiIiIiBxtCgdFREREREREREREREREZgiFgyIiIiIickIov/azRH750HQ3Q0RERERERI4DFnNUfmYib7obICIiIiIi8lwcIH73WuJ3r8WviNO74avg6F5HERERERERkUOlT9MiIiIiInJC8QZyJD99y3Q3Q0REREREROSEpHBQREREREROOIkv/2q6myAiIiIiIiJyQlK3oiLy/7N333F21PX+x18zc9r2bLLpIYVeAwIWxIKADUU6qD+9ol4QC3JV1OstXkXvVbGgggIqVlQEuYLBAnJBOiooNXRII71tPX3m98dJNtlsAtlkk93Nvp6Px0J2zsycz5w9s7uz7/l8v5IkSUMqWNNF47/8gOjJJVu/zQ6sR5IkSZKkXZnhoCRJkqQhEy5cydjDPtEb9iUD2bhYgmxmB1QlSZIkSdKuy3BQkiRJ0pBpfeWn+nQBDqQjsOHTP6E6aSyVNxxC5ZBZZH9xO0FPkcL7joVUNNilSpIkSZKGFceU2VaGg5IkSZKGTFCsbtt2QP0v7qx98o3fkrDhsrDxP35B8Yi96fzNZyB0mnVJkiRJkjbmlbIkSZKkEW/T7sPcPU/S+N5vD1U5kiRJkiQNW4aDkiRJknZJuT/8Y6hLkCRJkiRp2DEclCRJkiRJkiRJkkYJ5xyUJEmSNOoEa7tJMimozw51KZIkSZKkbZD0mWBCA2E4KEmSJGnUCFZ3MvagfyEoVwBIsmlWzbscomiIK5MkSZIkaedwWFFJkiRJo8bYAz5KWK4QAAEQFsuMm3HWUJclSZIkSdJOYzgoSZIkaVRI3f80YTXutzwoVYegmpFrQSfcv8LheyRJkiRppHJYUUmSJEmjQt0352x2eQCk/vIElZfvs3MLGmHiBHb/eZae6vq+y4QLDitxzoH9A1eA1QX47byQWc0J9y4LufTRFEkCn3pJhQ8faCArSZIkSUPFcFCSJEnSLqvuq9dR+MAbSJrriZ5cssX1Mv/3kOHgJtqLcNXTEYe2xbx0YsJLrsnSU9148JmAz96f4dWTixwwLumz7ftvTTNnQUQtROzr8/dnuGdphSuPLe/YA5AkSZIkbZbhoCRJkqRdUgA0fvU3NHz1N6y+/+tEqzu3uG559sydVte2eHR1wHtvTdNTDfjvl5Y5Ydbmu/UGy1t/n+avK9aHewl1YUI+3txQogFn3Z7m7pNKACzuhl88FTFnwQtfat70fAQYDkqSJEnadsmLr6ItMByUJEmStEsLgJa3fIGgvWfLK/UUd1o9A5EkcNatEb9dmGZ9F95Zt2f49bMVfnZMZbv2na/AK67NsqRQ229dCDcdX+D/FkX8dcXGl4rBFoLBmmc6ApIEXva/WeZ3rR9yVJIkSZI0XBkOSpIkSdrlRUvXvmBk1fyR77Pq9FfttHq21tSf5agkm1YecOOiFLB94eBLrsmyurRhmNB8DK++Pkd6gPffJsDpf0oxvyt80XU3du0zISfvHhOYJUqSJEnSTjWwqzdJkiRJGoFeLH8KgOD5FTujlK32lfvDzQSDG0z7WY6JP8nxsmuzdJQGvv/Vpc0PE1oecOdfyG1L0gPcJuCDd2aZ+NMcD68a4KaSJEmSpO1iOChJkiRp1AuA5uO/NNRl9HHRIy800EtAKQ5ICJjXFbLnL3PctniwLu92ZitfwOtvyO3E55MkSZIkGQ5q1Dj++OM5++yzB7zd5z73OQ4//PBBW0+SJEnDU3rRKpqP+BTE8VCXAsDAqgh4980ZkgR+Oy/kZddmmPiTHFN+muOiB0PiBH71dMiRv0kz+Sc5Jvxk+ARyMXDoNRmO+12a5T1w99KAnz8VUh4eXwZJkiRJw1RCMCgfo5FzDkov4uSTT+ZlL3vZUJchSZKkHSwAss8sY+yB57F67sW1hZUqFMrQOBRh2sAuUgtJwIwrcxTiDdtVEvjSAxm+9MDA97fzBCzqiVjUAwdeE7G+zo/dnTApV5uT8IKXVjhhlmmhJEmSJA0Gw0HpRcyePZvZs2cPdRmSJEnaSaKVHdR/7Aoyt80ltWglAEkIa27/H+K9p77wxpUqFCvQkIVCCYIAsls3H1+xAqfelOaBVREzGmMgGWDlSZ9gcIPhGgpuTtDn30sLEQBn3R5y+dwqv3lTmWwEPRVIh7UPSZIkSdLAGA5KkiRJ2mlS9z9D43/+gtTchZRfttdQl7NF9T+/vW9MFcPYV/0ba27/b6r7TtvsNk0fvpzstfdAnEAqqgWFQOXQ3Vk7598hnYJKlYYvXkPuV3fSEab5xqvfymWvewv/tE+Frz+YYn049mRHxMDDwZEUAg5UwH0rU+x2ZUQmgFICYQAT6xLaSwGHjIv54svKHDh2oK+ZJEmSJI0+hoMalo4//nhe/vKXE8cxN954Iy0tLfziF7/gueee4/vf/z4PP/wwAAcccABnn302hx56aJ/tb7rpJn784x8zf/58pk6dyoc//OFtruVzn/scN9xwA/fdd1/vsscee4xLLrmEhx9+mIaGBk4//XSSxD9ESJIkvaCuAi3v+Drh2m4AMn9+ZMDx186ypf67lv93Eav/+tV+j+V+eiu5a+7esGBdMAiQ/vuzNH3wMjp/8BHqvzmH+u/+AYAxwAX/+1PmNrTx9cIrtrKK0S6gtO5NEyewpKf2Gt29LOIdN4fcf0qBTDSE5UmSJEnSCGA4qGHrxhtvZNasWXz84x9n1apVPPjgg3zyk59k2rRpvP/97wfguuuu44Mf/CAXXnghr33tawGYM2cOn//855k9ezbnnnsuCxcu5DOf+QxBEDB58uTtruuZZ57h7LPPprm5mfe///2Uy2WuvPJKyuXydu97sAX+PUm7uPXvcd/r0rbxHNLOlrlrbm8wuN5Ie/tFC1eSfuBZqofMJJ2uDRcaBJD75R0vuF3m9rm1EUZv+Fu/x0576B6unb25cFADsSwf8LcVIa+a7NyEI4U/h6Tt4zkkbR/PIUmjmeGghq1iscjXv/51xo8fT6VS4YQTTmD8+PH89Kc/pbGxEYBTTjmFM844g6985SsceeSRBEHAxRdfzP7778/3vvc9UqnaW3zffffl85///KDUdfnllxMEAVdccQWTJk0C4Nhjj+Wd73znoOx/sLS21pNKedu0Rodx45qGugRpRPMc0k6zx6ShrmBQjNlrMkxq5V3vehcAEye2wszxcP8zW9wmHNdEW1sTTGqFuYv6PLa8sWWH1jua7DW5nrY2/8I30vhzSNo+nkPS9vEckkauZMTdbjp8GA5q2Jo2bRrjx48H4PHHH2fZsmWce+65vcEgQFNTE6effjqXXHIJc+fOJYoiVq9ezdlnn90bDAIcd9xxXHTRRdtdUxzH3HvvvRx55JG9wSDAzJkzecUrXsHtt9++3c8xWNas6fHOJ+3ygqD2S/yqVZ04sq80cJ5D2un2mkLzUQeQ+fOjvYsSRlb3YOHUI+hqaaCydA033/w73vWud7Fs2RpS/3E6Y3/7N4Jytd82CdDxX2dQXtlJ+sNvpvmOub3rralr4NtHvpkMCSVgZL0aw8tbZlSZSJ6VK4e6Em0tfw5J28dzSNo+nkPaWdraDKA1/BgOatgaO3Zs778XL14MwIwZM/qtN3PmTACWLFlCFNU65aZNm9ZnnSiK2G233ba7pvb2dnp6evrtf30dwykcBPzFRqNGkvh+l7aH55B2pvaffYzctfcQzV1I+WV70fzP3xnqkjYrphbTbRzVlfedQufFZ8O6c2b9sPJJAvHUcay672s0fu4qwqVrKb1mf9J/exqCgO5Pn0T1kN0hgdKrD2DNzReQu/ZuVgdZfnT4URw/fhyn71Hk0odDfvpMet2zJus+wp196MNYwuHjY46dGvPnxSH7jEk4eFzM42tDDh4Xc9Ksqt/LRih/Dknbx3NI2j6eQ5JGI8NBDVthuOEPIckL/IRe/1g6nSaOa/OLFAqFLa63PYJ1rXg7av+SJEm7vGyawjtfs9GC4RcOVhuzrH76MoJnl9LyT98iyJfo+cQJFN911Atul0weS+flH+r9PL+l/e83je7/OJ0scA4AFQC+9qoqX3pllQdXBuwzJmGPX2YGWPn630c313040no016sdUwTcd3KBqetuuv74wRuv079bU5IkSZK0ZYaDGhGmTJkCwLx58/o9Nn/+fAAmTpzY2zm4cOHCPuskScLixYvZfffdt6uOlpYWGhoa+u0fYNGiRZvZQpIkSSNJAqx+6lIIQ5I9p7D27q/s1OdPh3D4hG276SwEzty7zA+fTK9bEqz7b8Idxxd4zZwscb9+yOFg41Bzw7FPysXceWKJMITG9GY3lCRJkiRtA8NBjQj77bcfbW1t/PrXv+bUU0/tnXewq6uLa665hra2Nvbbbz+CIGDKlCn8+te/5rTTTiOXywFw4403snbt2u2uIwgCXve61/GHP/yBZ555hj322AOoDXt61113bff+JUmSNHQSoP17H4R1N5yNNKftUeXLR9Q+usvw62cj9myJOXJSLXBb+p4i8zoC/rEyoLOYcP5fc0Nc8Qb3nlSgUA3Yv9XROCRJkiRtreF24+PIYTioESGVSnH++efzb//2b/zTP/0TJ5xwAgDXX389K1eu5Ctf+UrvMKSf/OQnOf/883nve9/L2972NpYvX87VV19NS0vLoNRyzjnncOedd3L22Wfzzne+kyiK+NWvfkV9fT2lUmlQnkOSJEk7X5IKKZ/4iqEuo9fEHCzrP5r9ZiTsO6bK148o9y5pSMN79uk/3ObM5oSZzbUA7vy/Do+hRg8ZW2X3Zti4a1CSJEmStOM4u71GjGOPPZZLLrmEtrY2vv/97/OjH/2IKVOmcNlll3HUUUf1rvfqV7+ab37zm2SzWS655BL+/Oc/85//+Z/MnDlzUOqYNGkSV1xxBQcffDA//elP+fnPf85b3/pWTjrppEHZvyRJkoZG3DR8OukAbj2+xAsFZm+dXubkWRXuPanA7SeUyQxaw+NAQ7pkm7YJSThpRpmbji+/+OqSJEmSpEETJEni7ZnSLmjFis6hLkHa4YIA2tqaWLmyE3+aSQPnOaThoG3Ce3ZK79rGs9ptSed/vZ3Ch9+8VfurVCr88Y/X8d73vpelS9cQRTtmUJaLHwr5wj8y9K884S8nFZnVvO0n78UPR3zh7+k++06R0JhJWFsayH2kCc+9o8CsX+Y2U+eWt1n+nq1qi9QuzJ9D0vbxHJK2j+eQdpbx45uGuoRd1rzgy4Oyn5nJvw7KfkYSOwclSZIkDZmd9XeYrq+/9wWfqzK2cauDwZ3p3Nkxz7+7QH0Ys+HVSvjPw8rbFQwCnHtQlf99Q5G9m6tMrKvyrwcXWfyeAj89ukz/r8wLP1dDBh47o8DMhuqLritJkiRJGlrOOahRp1wu097evlXrtrS0kE6nd3BFkiRJo9eO7BpM1j1B9ydOoPDuo2j4xI+2uG7xna/ZgZVsn3QI895d5PnugMXdcEhbQnqQbvN81eSEO0/qO2/2KybG/PKYImfemqUY175GHz6wzCWP9O0yXG96QwzAuBz89dQSKwuw/69euItwUp0BoiRJkiQNFcNBjToPPvgg55xzzlate9lll3H44Yfv4IokSZK0o6xc9pPef79QEFk66sAdX8x2mtqQMLVh5zzXMdMSFr6777CfD6yIuHPZphMbJlz3pr7hYlsO5ry5yNm3ZlhaCDbqIwyAhMl1Cf84rbiDKpckSZI0WnjL4bYzHNSos/fee/Od73xnq9eVJEnSriGJQoJq3H85UHnNATu/oBHm2jeWuPrpkC8/kGZFPmD35pifHF1mWmP/dV8+IeHBMzYEgKvy8NjagJe0JTQ4MIckSZIkDSnDQY06zc3NvPzlLx/qMiRJkrSTlY47jNycvw11GSNWEMAZe8WcsdfAu/7G1cGrHEpUkiRJkoaFQZqpQpIkSZKGt65vvp8k3XdYzATo+o/ThqYgSZIkSZKGgOGgJEmSpFEhaapj1UPfovvMo6mOa6K871TW3Px5Ch9961CXJkmSJEnSTuOwopIkSZJGjWRcEz0XvoeeC98z1KVIkiRJkrZDQjDUJYxYdg5KkiRJGjLOQidJkiRJ0s5lOChJkiRp1+RNpJIkSZIk9WM4KEmSJGnI7Mj8rviWw3bg3iVJkiRJGpkMByVJkiQNmSTctngw2ej/xdccQPt3PkBlfBNJAHEmRf4dr6bzhx8dtDolSZIkSdpVpIa6AEmSJEmjV+eF/0TL+T/Zpm1XLP4hpKLez0unvXKwypIkSZIkaZdl56AkSZKkIVP6p6PJv/UwEuj92BpOJyhJkiRJo1tCMCgfo5Gdg5IkSZKGVNcPP0pXkkA1pm3K+7Z6u6Cjh2Rs0w6sTJIkSZKkXY+dg5IkSZKGXhD0GSL0xSRA0li34+qRJEmSJGkXZTgoSZIkaXiI461etbLHRMg4EIokSZIkSQPl1bQkSZKkYSFY073F2R42nouwsuckOv73X3dGSZIkSZIk7XIMByVJkiQNC8nYRhLYbECYAKuW/RhKFcimd25hkiRJkqRhJ9ni7aV6MQ4rKkmSJGl4CF7kwi4IDAYlSZIkSdpOhoOSJEmShockefF1JEmSJEnSdjEclCRJkjQsBKu7HBRGkiRJkqQdzHBQkiRJ0rCwfs7BzYp2ZiWSJEmSJO26DAclSZIkDQ9BQLW1frMPFU54+U4uRpIkSZKkXZPhoCRJkqRhY82D36Jan+nTQRhnU3R/7X1DVpMkSZIkafhJBuljNEoNdQGSJEmS1CuXYfW87xM+v4q6i+ZQPnwPSqcfCaH3NUqSJEmSNBgMByVJkiQNO/HUcXR/7cyhLkOSJEmSpF2Ot99KkiRJkiRJkiRJo4ThoCRJkiRJkiRJkjRKGA5KkiRJkiRJkiRJo4RzDkqSJEmSJEmSJGmECYa6gBHLzkFJkiRJkiRJkiRplDAclCRJkiRJkiRJkkYJw0FJkiRJkiRJkiRplDAclCRJkiRJkiRJkkaJ1FAXIEmSJEmSJEmSJA1EQjDUJYxYdg5KkiRJkiRJkiRJo4ThoCRJkiRJkiRJkjRKGA5KkiRJkiRJkiRJo4ThoCRJkiRJkiRJkjRKGA5KkiRJkiRJkiRJo0RqqAuQJEmSJEmSJEmSBiIhGOoSRiw7ByVJkiRJkiRJkqRRwnBQkiRJkiRJkiRJGiUMByVJkiSNaEkCT66CrtJQVyJJkiRJ0vDnnIOSJEmSRqwbn4UP/rpCDxFJAG2pKg+dFxGFzj0hSZIkSdLm2DkoSZIkacQ69+oyXWGKOAxIgoAV1RR7fK061GVJkiRJknawZJA+RiPDQUmSJEkj0oNLEzqiqN/yniDiorsMCCVJkiRJ2hzDQUmSJEkjUlMaYjY/fOhF9+zkYiRJkiRJGiEMByVJkiSNSEFqy/MKFrzUkSRJkiRps1JDXYAkSZIkbZMECAJI1s0SEQR9/y9JkiRJkvrxdlpJkiRJI9LqrhjiuBYO9gsEE+5fMiRlSZIkSZI0rBkOSpIkSRoRkiThtmdifvlATLGSkFSBKNhsp2AqTvjnqys7v0hJkiRJ0k6REAzKx2jksKKSJEmShpXbFwWcdWNERxzSWCjxL/uUeferUxx5WZVllYhMkvCZP8ZMaoYwDImrcb+AsLVSZUXivZCSJEmSJG3KcFCSJEnSsPHE6oBTfxdR316ktVSFAL78SIpfPF5hWTliZqnMhHKFACgUAuqacnRXYwIgCTeEgRNLFVY05OgpQ316yA5HkiRJkqRhx3BQkiRJ0rDxmTtCmlblaazGtQUJtHaXWRYGtIQxa1MR87JpsknC9GKZad1FVqYj0sUyPXVp4jCgUEl4tD4HQcCjyxNeOnV0DhMjSZIkSdLmGA5KkiRJGjYeXFihYX0wuE4A5OKEzlRAPoogHVIMAp5KhezZmaeaJKye3ESSjmrrxwnJ2iJUk9rHKJ1DQpIkSZKkzTEclCRJkjRsxPl4i4/lowga0hCuC/syIQurMcX6DKwLBgGSMCBdFxG2F9l/ovMOSpIkSdKuKPFG0G3mlbIkSZKkYSOdJBTDvhd4CVCMAkiHG4LBdYpBCKn+F4SVTERLucKq/I6sVpIkSZKkkcfOQUmSJEnDxvhUzNN1OerLVSpRSJAASUyqmvQLBknW/b8UQ26THZViogTiLTciSpIkSZI0Ktk5KEmSJGlYmL86YW0hoa4S092QpZhLU6hLU8hlyFYqNOfLkCQbNggDIoBiFfKVDY+VqkSdRRJgYbvpoCRJkiRJG7NzUJIkSdKw8OFrKqSTgHwu3feBMKCUTpMrVmjsKNKTCknSEeMqVSZ353m4PgedJegOIACqCY2lKk1JQkfXkByKJEmSJEnDluGgJEmSpGFh/rIK+VSKZDNzyndHIZ2ZdaFhDBSrNOeLVMKA8eUq00plVqQjykFAS7lCNqnNX/j6/ZygXpIkSZKkjRkOSpIkSRoWOtIpylFEWKoSZ6LawiCAJCGuJv3WX5JJEQcBE8oVMknC1FKl97EYeD4TkUlt3UwKf32qwhXX5+nKw8sOz3Dem7ODcUiSJEmSpB2k/1WitpZzDkqSJEkaFsbGCZUgIClWoatc+yhU1s0n2H/9YhjSUI1ZG/a/rAmAnjDi+a2Yc/COR8pc8b125ncEzA0y/PqvVY7+755BOCJJkiRJkoYfw0FJkiRJw8LYuArpkCTYaCjQcgyVmFRc7bd+HASsTqfoTEUsS0d9HguA6cUyjy568XDwiqu6+EdzA39vbuT5XJYnGup5NpXlB3998W13uDgm+q/f0nLQp2l54xdJ3fvkUFckSZIkSRrhHFZUkiRJ0rCwaEw9FPu3COYqVaIAKnURmXyZUhgSJEmtmTAIyCYJ48r9w8O6JGF6S99lTyyN+c//LbFkVczYqMqhUxPaiVieTjO2XGZNKkUSBPREERffU+GfXzZ091MmPSUa9vt3yOdJKJNZtpQxb/tvut9/LPkvvfvFd9CVJ3vNX6getBuVw/fY/DqPLYNx9TChaXCLlyRJkiQNW4aDkiRJkoaFzlwaiqV+ywNqQ54EAdRXq0wplSkFIQlQCWBFJk0lCEglfYPFEpCu2xDuLV6bcPb3Cszu6GEyUAX+WKxnQhBzUL4IQKpaYGkU8UxTPUsKIX+4q8Cbj8ztqEPulcQJyz92F11znmNca57xnz2M7rtX8nx+ArvzOCk2zKfYcMXNFP71ZOIkovpvNxP/4UmClhTpn50CB00FoO4r19Hw9RsIqHVhxg1ZVj3zbQhDMlfeQXjl/SR/X0GGCgnQPX48ySOfqL3IkiRJkqRdmuGgJEmSpGEhXSjXoqqEDSFVkpCOE/YqlVmQJNRVY+ZnM71DjwZJQkOl0m++hAqwOgr5519X+b+zQ8IA3vGrmNkdPb3rRsBehQIrcjlWpFMUwoBxlZBJpQoNXXlWpSI+9X8Jb3hFQhTt2NDs4T2uo1RIWDhrFismNNNwSYFj5j3NvjzZJxiEWlja8NErWHl3xPj2J8nSA92QP+ZZOn98FuFxB/QJBgHC7iKZI75A06IlhOu6LBMCyjSQENG4Yjmdx/0A/nDWDj1OSZIkSRosCd7cuK2cc1CSJEnSsNDYXqChGsMmH23VKimgJU5oT0V95iRMgoBKEJLZqGuwDCwNA0oBlJdWmPW1Kp+9vkTH6ioh0B2GzG2o4/H6HOUEHqvPsSCXoT0V0ViNqUQhdUnCtHKFpiTg/sfLO/S41/7peeLuKmEYsOfTSzns/mfJ12XpDDOk6d9JCRDMXUZb+xOElJnPLB7nQDpoovHMywj/+mSfYHC9pucW9AaDAAEJKXqIqBJRJnP/cy9aa1LqP3yrJEmSJGlksXNQkiRJ0rBQJSFdTSAKyVZjAhImVGLGV2MAurIRPUn/+xvLYd8gLA2MSeD5hgyNxQr1ScL3HwtpiGPuaapnXl0Owtp+Ug1VKlHt3+PK1X53T7bGMfO74GWDfrQbLPnCQ+QoUSlnAMgWK7z83qeob+zZ4jblnoiAgPs5kjJZAJYylWnMY/wji1jXfrnJVv1fu4B43f8hQ3kLUSR0/vxJuj91G+PLy8hQojq9FW79IDTt+CFXJUmSJEmDy85BSZIkSUPuq3cnrE2lWRuFEIVMimMOLpaZXK11qpUCWJHLEG9mTrzNDSSTAda21PHUlBZWTWyiMRvRHYa0p1I0lqtQrUKSUIkiiGOoVFkZBqxZFxrGQS2MLIUBj7RnduCRw56P30mFvs8RAIUkx+Yv2UKSFe3czyt7g8H1FrMbcUceiKkFhOvFfYbcSYCYNFXqNnrOzXcFVruK9HzsVvYuP04bq2imrE4ccQABAABJREFUk9YFC0jv8QXW7nYx8ZNLB3K4kiRJkqQhZuegJEmSpCE1by189e51nyQJxAnzs2kKYcCYSpViGLIkk6KajSAVQFffOfjGlyv99pkPA1hbhHE5ojgh013ikHK19wKovRLwRG7d3IVxLUQrBQFPZlJMi2BJWwPVKIQk4aHnKpyxusr+Y5N+z7Mtgo4eslfdSebWh6jsPYWQVWyu02//7idZyjTaWEKKClUiiuSop8hqxhNSJSbqs01MRHLx3ZRJ00MLGYpUiSiQo5mVvWsnpIg3CRYTqL3+GwWwxbmrWXvBfbSymmhdlyHAEiaxiGkkxZAVr/o/pn1wKvUfOozyu6+m+uga4snNZK4+nWj3MYPymkmSJEmSBo/hoEaF1atXU1dXR11d3YuvLEmSpJ3qkeXUGuQCIBX1NrwtS6dYlklvWLESQ326Fl71VKBaWzFbjamw4eKmCizKpmv7aS/RlIIJlbjPxU9LnNBWrtIZBhTCjbrzgoBFbfWwbqhRgoAkm+I911b5yatK7L/f9nURBsvWMu7gfyFYF0hm/+9hYkKmMJ/FzOxdr42lNNNBRIk1jCNNkQwF6skDVWbyFLvxDE9xEEuYvuG4WM2qzhYqhExkQ0dfPd2sppk22mt1UAFKJKR75ydMkdA48VMUgizhuYez8NLVUK7VmWbDvIsFsixkNzaEmSHPX7qIGZfey2rGA2NhAWRe8QuaHzmTaEL9dr1mkiRJkqTB5bCi2uXdddddnHLKKaxZs2aoS5EkSdJm7N0aQxjUgr+Nm/M2HUK0uwLFKuRSMDYHjbXgcGk2zcIw4KlchmdzGR5oqKMrWtcjV45pL8b95iUEaIo37bsDUuGGYHCjOuYnKf7zyjz/9aVVW31c0cMLCJ94vs+ylndf1BsMrrc6amV68DR1DStJ6ou01C1mfx4AIEsP41nKGNqppwJE1GZVDImI2YcHOZQ7mMQCUlGeJc1tPNm0N9V0n6cgJCa9UWdiAKQo0U2OFUxgLa3EhKSp0JR0s+LbC6jlgQEQ0MHY3i9NF41s2uWYEJFQIVo3a2EClEjTddL/wqK1hGddQ/Svv4PuLc1qKEmSJEkDkxAMysdoZOegdnmPPPIInZ2dQ12GJEmSNqNUjjnj+2VoqieK4y3MereRYhWy6yK9ZMP/OrK1IUKLmwZ7QBLDk3VZDugp0hhvGBqzuVr797OZjbapxgRxQrJJmPim+SuIKjHLVyV89PyVJEATs+noXsxrj8oydeKGdYPnVzHu8PMJ1u0/n8ry2O4HMm3pfMKOlf3qKycZfjftSNozzeuWTKCrI8sRK/5BivVhWsSGQC5Y93lMALSwlhbWsiCazv3plwPwl/TLeGXnPUwsr+h9nhzd/Z67kXYWsTcAK5jIDJ4lQ4FwoyFEAQrUs5ZWxrCGOnr67ScgppW1NFGgk0YiKpTIEj+1lrpDv0qtVzFF+YcPkzp+FvFlp0I6giQhWNZJ0tZQ6xqVJEmSJO1whoOSJEmSdrgkgd89Bdc+BpMa4ZFl8LfFkC5Xaa1CS7nKzPYeHmyu79s9mKz7ZH0XYbDR8kJtrsEwhLZSlQnlCvkwZHkmxar0Jpc6QcBzuTQH9RRJqD1FFhhfjUkVy6xIheSjkEIYkXSVoClTe84kYf8VnbQWa89VAYJiN/96+884eMlTFH6eZs4Br+XHMw/hn/42h/1WzKcShL3BIEBdpcS+zz5FtlqkGtYRxXm6aWYV0+hkHNncko2CwZqnm2by0lUPko7XH/Smd7OuX5awJDuRBXXTycVF2sorWJkeX+t2zM7oDQdLpOmhnuy6YUXXK7NhmNQqaVbTyjSeYXceJU8Dz7EPMRlq8V+KBMhQZgJLWc5EICAgZhqL6GEMqxlHiSy12Q+rFMjRSSsTWEIra0hRZumcFaTnfIM89fTQQEJIKQyJ4oQKITEhYRqmfHIvGn90O6klayiEdVROPZjsV18Pdds3tKskSZIkjXaGg9qlfe5zn+OGG24A4G1vexuHHnoo3/ve97j55pu5+uqreeKJJygWi0yYMIFjjjmGD37wg2QytT82nH322WSzWfbbbz9++ctfksvluPTSS9lzzz23antJkiRtcNYc+O2T/ZeXUilKcZlZnfnakJ6NaegsEyQJ04tlxpdrodyyTIqFmfS6zsEK5Gs9hkEUUC1DRyqipRrTHMc0F0qsjSKqm3T/9YQhj9Zl2D1fi6/Wa41jGqoJD7c1QGcZijGUCswu5JneXaSpVOldNx3HnP3X6zh4yVMA5KplTnvoZqI44YDli8hWYxIKmxxlRH2lq/avpBZMNtBBA3PpoYnF4YR+r0sShFSD9d2C6+PMjY+ntuzpht35a+vLNqqvTFSsrZmnjrVBCz008HS0JwdWHuz7HMByduuzrLzRK9NBKwlpICBNgQzdhEBIhZksYAxrCEhoopM0ZeaxF+tf2Qrp3n+XybKImaxlDDN5juk8x1rG0s0YMsTExARxijxpSqTXF0Lj//yOxnVdirm4ROnqe+j+41wyt7yfePrYfq+ZJEmSJGnrGA5ql3byySfT3d3Nrbfeysc//nF23313rrvuOr74xS/ymte8hnPPPZdKpcItt9zCz372MwDOO++83u0feOABFi1axHnnncfzzz/PrFmzBrT9UNt0mh5pV9PbROJ7XdomnkPaWRa2bz4YBEiCgLWZNHuUKpQSCEtV4nTA5EKFSeUNodyUUoViELA8k6ZxdYFia45yvkpSjokSGFupUgqgI4CQgInlMouzm9y0FQR0pVI8Ux/Qkg7Ip1O05kuUopBljTkqqZAgqpBUYkiFlIKQXKXvQKeZasxhzz/W7zgmdK0iWy3Xnqbvk1LrN+y7ZL16OpneXSBXfQmFKNe7fGLPCsrVDBvaKJONtk6AmASY27R/n32XwzRJKiZdjsmX67kn9arex6pB3zkdVzOBTvqGbI2sBaCLZlZsFByWybGG8TQxv3fZGDpZnBrLX8a+gkKSo65QZVJXJ+kkXjfgad9jbqeVpRSYyFJWMJGE2nCutcCxSom63vVz5HuDwfUylFnYUc/Mb/6J/EVnoJHPn0PS9vEckraP55Ck0cxwULu02bNns+eee3Lrrbdy1FFHMWXKFE499VRmz57N17/+dYJ1P/1PPfVUTjjhBO65554+4V4+n+cLX/gCBx54YO+yK6+8cqu3H0qtrfWknLdFo8S4cU1DXYI0onkOaUd7sqsKm5mnbr04CFiZClmUzRAXa8NxthYq/dZrrVRZnkmTqVRJrexhbSZNJo45oKfQ58JmTQipalwbenQzf+3pjiLqkpg1dWnCJGFSZ4GmfJnVYUBQTVg9voFSXZrHW3M8M6mZ185bwfieEiQJ9eUyS5rGs9eqBX32+djEWey7fBFtPR2bPFvCi8kkZd68+E/8vfUQ1maamZxfSuvqMlHvDIzhuo/1anMOBkApSPfbX7paZY+OxSRJhcXhtN7lz4fTGF/dMOdhKyvopoUOxgEJY1jJOJbUXiNa+u23h+Z+/YsrovGUe+qpK8ZAxJJUCxMqnYRbOOy1jKGR7t5gcL1NZ4qM+y2pqZAm+9xKGtr8vrUr8eeQtH08h6Tt4zkkjVwvfrWlLTEc1Khz1VVXkc/ne4M9gDVr1tDU1EQ+n++zbjabZf/999/m7YfSmjU93vmkXV4Q1H6JX7Wqs3dKKklbz3NIO8vMOsileqcI3KyFdVkqG8VOpTCAuO86pTAgHcdk4mRd31zC+HKl30VNU5zQmQppKFfpTkd9A8J1geHY7hItlZi2nhIAK8OAMAnpqU9TqtsQuJWjkL9OaeXUuYuoK1dIJQlXveTNfPLPPyJXqW373Ngp3D7rUN59303rtoqAvh2HL2ZsqZ1jl90GQEzAMxxAE2vWPbrlX+pm9sznyaa9+xzfhPYuxlaWMIHFFKhjdTCWgISeeAyrmUAry9f1H4bsxlMspsgkFhFt9IJn6f97bYZ8n0qqhKyKWskUN2wXELAmamBCpYMq64dFBUioEpChRIr+b4SAhEZ66KIBgBJZVjOGses6GQG6qCcmpPDyWRRWdm7xNdHI4c8haft4Dknbx3NIO0ubN7ZpGDIc1KiTSqWYO3cuN954I/PmzWPRokWsXr0agMmTJ/dZd8yYMYRhuM3bDzV/sdFokSS+36Xt4TmkHS0dws9OhDOvh+7yJg+uC+sq2ag21986z2fStFRq8RLUBubsCELGFsu9cVOQ9O84gw1xVDUMNgSDcQLxhv0vy6bZrbihmPy69SqZ/iMvdGbTNJZq63anU9Rl6vj6a95Fc7GHjlwDf9vtAGYvfppw3X2rCSHBAMPBjYUkjM0sIii90Fq1oUVf0v4AITEL6nYjF5fYp+Np5pX3Zym7MYXnaapWqWMNbSxjLW0sZg+WM42xLGccKwmokiLuEwzWYtcydXSQpxmAYN3cgF000Eg3eXLMZzrdqTpy5b4pbiWICEhIUSUmIACq6/4/mSXU0009XfTQ2LtNlZB6CtSTp4t6cpSokGIxk8hRoIc6Omhh+ksr5M89xluEdzH+HJK2j+eQtH08hySNRoaDGnUuvPBCrr76avbZZx9mz57Ncccdx8EHH8yFF17I0qVL+6y7aTA40O0lSZJU89qZ8Nx5MG8tNGVgfjv8/CG4Z17MvNUxlWy2z/o9ATyUzTCuWiUJa910mXXhXgJ0plOMK1epq8bE9A0Ju4KAGChGIfWV2lyF1U3+4NMOjA8DGtZleCmgDGSKVbo3qT1XrjC/pYF0nBAl0JOewgkPzyVTLrFg7CReumAuQV2Ghw6cTdhdISqVmL3goX79fsm6j01/w9x0qE6AfFRHKeiGBFYyhYksJeoz/2AVSIhIOLT9AQ5tfwCIaKeVedQCxjxjiEkRAgXq2Z3HKVJHhiJpygSUqRKxiilkKTCGlSRAiXq6aKNEwFgWUybNSsZTpI3VtBEQkxAwlSUE6Srk+1afTiqEQEJCQEIC5Cgwk+VkKNFBCxATtIaUOwOSpgzRHs3EnUVyE1NMj3qId59M+j/eTfKde1j14/nQnGXKRa8kOWI6kiRJkqTtYzioUWXJkiVcffXVHHfccVxwwQV9Hlu1atUO316SJGm0mzmm9v9x9XDoZICId/884calMdSnSOcrpCox+SShFAYsCdddsiQJuQiipBb6VYKAmcUCzXFCVxgSh5CpxuSDgFWpkEIq4n1PzWN6T54HW5r4392m9C0kgaXZNK3lKiEwJo5ZEYZk82XqO4v0NGYgCIjKVZrbC9RX13cFQrYxovtjx9Ox5E989CPHs7SrQBSlgJf37j7/zm+Tu/lBEmJWjBnP2ncfy5RxMY2fu5paPLiha3FlZizjS6v7lPdM0yzu3P2lTH+0myL1zGdPJvM841hGE2vZfOtcwDz2BBJ2Yx51dBJSISZFD00sZgbjWEZEiSIplnAAeeoJgEXsyVrayNFDD7Vhj9JErGYyrSyhSG6jly6kjgIt9PDyNU/wcGZPqtXaEKJRUmVspXtdNQBVslSokGUx04mBCW9tpumHp7I1gysFnzqK8Z/aihUlSZIkSVvNcFC7vCiqDQ0VxzFdXV0A7L777n3WufPOO1mwYAETJkx4wX21t7dv1/aSJEnq7/unR0z/TkAQBRzUmadCwkO5vp2EBAGFIIBUre9uZr5Ec5wQA41xzIpUiucacpSD2oCe/29dMAgwvrj58Tm70hFzm+uYVCyTpEO66zNUEojDgN1Wd7MqDihHIXt39NRKIOGkN9dx1DGNVCoV/vjHBBrroKvQb9/dv/hobwdiBIwDikA8voUxH/4BkKbW/RfTUMlzf+tB7N35LAkBc1v2YXm2jcMffYKF1OYTLFDPc+zFIqbzSm4joNin2zABnmc6acoczH2MpRY2TuEpnmdvEiJ6aKCHPWBdN1+FFMm6Hr8UFbpopYtWUhSoYxVdTCYipotWduM5VjKJCika6GbSuv2n4pgphQ7a0xmy5SpZqhvNMgjJa3en7ZpjiFfkqT67ltSBYwkaNvnaSpIkSZJ2KsNB7fLGjBkDwM9+9jOOOOIIJk2axI9+9COKxSITJ07k0UcfZc6cOWSzWbq7Nx1Eqq/dd999u7aXJElSf5kUtZgqCkgnCavSUW2uwE0nfwlrsdOYcoUJlQpQ605blEmTi2MaixVWtuSgLmLPjg2/l00pFNm/vZO5LRv1qqVCCAK6UxHPNWWIs30vjQrVKj3VgN16CiwII44/qo4z39qw3cdaPu0ISj/8P9L3P9sbotXHedoK7dzWcDRUUqRLBYr1adqDcf0aBCukiYnWzcUYsX5Q0oCQaSwiYdG6/dYCwEby7MkT5MmRppseGimTZRm7rQsGa69ihRQTWURuQkT2DTMonP9Oej50N9W7F1MkR8I49udRehizbvbAmk6aiAloCQtkXzWB/J0rCaA2rGs2y/QrjwIgHF9HOL5uu18/SZIkSVov6TdBg7aW4aB2eW984xu55ZZbmDNnDvfffz/f+ta3uOiii7jqqqtIkoRp06Zx/vnnU6lU+NrXvsZjjz3Gfvvtt9l9ZTKZ7dpekiRJ/YVBwPSowrNBhmIU0FKJ2Scu0RMELE5FVIMAgoBsHLN7sUxzNe7ddm0U8nw2XfskFUBD7d9r6rKMyxd71ztt4WJ+k57GQxPGUF+uElUT0qUy9SQsbmyAagIhpKsxJFAsJxzQVWBhNuLosTFnvnVrBsHcOu1/+A/ChauI5i4k9fA8ur55ByuKu9GQB6gAKdpKFYKkum5+vw2zFI5nGVHvkKQhm85WmABrJ0+hdcliqtQBERFVGukGEupYRhfNLGXGJlUFJETEj3ya/LolbdcdB0DPH+bT+Z7/o4cmsnSxgklUieimnh7qyNDDpIfeRdBaT+PclXT99AnSr5pG/VumEwRerEuSJEnScBMkyaa340raFaxY0TnUJUg7XBBAW1sTK1d29msukfTiPIc0nMxfC8d8r8K0cpWmUpUK8HRdlvZUBElCKk5oK5VJBwHjyxXSQHsU8kwuQzkMqavGxHURxZba3Hj7LF/L6Q89S2rdm3tZXY7vvWJfcpWY455eSpTA8voMt0wfTxIG63K2WpAVxjFTlnYxrlBmVSriR6cEHLx/pk+9tWFFr+O9730vS5euWTfn4DbIl1gy4xJWMb3fQy2sYBKLyVNPKytIUaWePBEx7TTRQmefLj6ACiErp+zNhMVPENO/0zGkQELMgxzJpsHiDJ4hXv65zZa5fN+fw+o8rayijm46aWA1raR3q2P8vf+PIB1udjvphfhzSNo+nkPS9vEc0s4yfvzg3Wiovh4MLh6U/RycnDso+xlJ7ByUJEmSNORmjIGZPSXq1v1hZkE2UwsGAYKASlTrHlyZTrEwmyYE4nVDj+7TU2BMNaa7EvHIunDwiQlj+PaRB3LAstWMaS9w04Q2su0F9u8uEK17jqdaG0mCgA3jYJahmhCnQgoNGTrKVRrimCeLaQ7eUQdel6H7FYfAvav7PVQhzXgWk6ayySMp6l46nvhv3USbjDvaTivB2/YguOxx1g852ldCRMwkFvTpHqyjm0bW0rGFMsc/+g66//0uOq57lnyuSsO/zGbyGYcQ1nlJKUmSJEkjjbd3SpIkSRoeNsq5OlL9L1W6o5Dx5QoEQS0YBNrKFcasG2a0oVRl6pqe3rkKuzMpnsnVs/jAcSz8VEQuX6K5p9y7v7g3OEugqwSluDa8aLFKOwGpJKECZKIdOzTmzJ8eSb/JBUkYM6aHVL9gEKBK5fBZhOvmFVyvQkSUSYg+8GoCIKTUZ6tyEFCk1gE5hfnswUOMZQUTWcpklhAEW748DKKQxi+/mnGPv4fmB95HdObhBoOSJEmSNEIZDkqSJEkaFhI2hIK5uP/YThUgHwak4oQwjslVqjRsst60NXlmL1hDw8oeKmuKvO/oNDe9L0UUwml7xjydzVBdt+4ea7tr/ygl/bK5cgLFKCQfBrxp/x172ZQak2XsadMIqQIJIVVmRs8y7odv7BcZAiQkVI7Yj+6P1+YErBJSJAtBQvmJz5NMHUcChJSJ6CGkSEiev+31SvKfehMrmEwn40loZAzdNFAgQ5Hur5yyQ49TkiRJkgZTQjAoH6ORt3pKkiRJGhaK6YhnxtTT2lGgENaGDF0/D2CUJOSjiHwAxAkkCQVgSRQyvgzRRvspEZBPgHTIIW1V1g+tecEZ9Tz8+bU80FDHXoUSY/Il9l66lqfHNBJvWkySsLw+zQ2nJ2RTO/5iccp3jmDihYdT+flD1LVB/KYTieuzlII0uWRDt2MCJGGK0mv3hzcdQvHdryXz63upHDSdytEH9q635sZ/o/UtX4ZKTCWIWPmS2ewz52Sq6RDOP47Cii4yJ/+Q6MnlhJmE4hdOoHLmETv8OCVJkiRJQ89wUJIkSdKwsLw+QzUdsjKd6g0FAeqqVXLAmiiCOO4dNhSgFAQ8kU2TCgJKYUBLJWZ1KuoddpRS33n3fvNfY3j8/m7mPlRh5gH1zDoox9GXFng+zPSZni9KEiYkMbOn7bxLpqghTXT2YX2CytJFZ5L5l++TEBCQQJRm7c3/BXW14UHjqWMpnHdcv31VXzKLlYsvh54i5NKkw026H8c3UrrjozvwaCRJkiRJw5XhoCRJkqRhoVRJaGkv0B5GfZZXgoA1dSEU6RMMrtcZhpCqbdMdRaTiuDaBQiVmn4n9u/72PayBfQ9r6P38iI41/HrKVChWIU4IooB9O7p435tzg3p826L4zldRPOXlZH7/dyp7TSE+cLeB7aA+u2MKkyRJkiSNWIaDkiRJkoaFaYUSnVFEe7ZvOBglUK7LQLkM1YB+EwQGfQPAShBAAqlqTCl58SFB//OdDaQum8dD41tJV2OmrOmi+NJxvPvwYXK5lE1TOunlQ12FJEmSJGkXMUyudiVJkiSNdk3VmLo4YXkmRXWjwG9Sqcy8IAcNKeiIobpJOBhuJgCME+IwYFV7wszWFw4IJ+9dz9f/J8f8R7tZWElx8OyJjK0bjCOSJEmSJGn4MRyUJEmSNCwkIeTihGnVCvMbchAnkA5Z1JIhyFdIGjK1LsEo7B1etL4a07Np9hcnkCQkqZD9prx45yBAOhey52FN7DnIxyRJkiRJ2jH6TzqhrRW++CqSJEmStON94q1pYmDJmDpYn+mVYyqFKrlKDGuLtWVBAGEIYUhPKiIigTiuhYLVuBYcBgFJAvnK1oWDkiRJkiSNFoaDkiRJkoaF1x+W5ulxudo8gRsPHRonlMIQqgkN1SqNlWpv52A6Tji4s0BbqQLV2vJUkvQONbqi23tJJUmSJEnamMOKSpIkSRo2gjCodQBuohon7N9TIAYWZdOkEohJmFQqkwb2KFfYrQzxurkKH0ynSFWq7Ds+2rkHIEmSJEnSMGfnoCRJkqRhozmKoVztt7yuGpOJE56oy9IVRVTCgDgI6EhtCP8yQC5JSCcJURwTpgL+9mz/fUmSJEmSNJoZDkqSJEkaNiYFVYiC3mFDAQhgYr7EmlREEvSdQ7A9FbFp/BcAcTWmlIpYmd/hJUuSJEmSNKIYDkqSJEkaNqpFaO4uQV0E9Smoi2isVCgTECX9hxsNqYWBm2qKY4gTDp7usKKSJEmStCtKCAblYzRyzkFJkiRJw8bMqRHhw0VWFiu0RwFhAo1xwpJsit1KFXJxTCHccI/juHKl3x2PIZBOEoJylSlNhoOSJEmSJG3MzkFJkiRJw8a/vzHi+VyKMXHMHuUqbXFCezrimD0DnqzPQgLpOO4ddrQ9E9G/nxAmV2KSBB5dvrlHJUmSJEkavQwHJUmSJA0bU5rgmg9kmPqSLM+MydEzIcN33x7xw1MjvnAUkIJUGNBGzLt2L1OuJqxJ9+8OXH+h013amdVLkiRJkjT8OayoJEmSpGFlj7HwgxP7B34fODzgA4evnw8i4uEVEb+al7C0oY7WFV19ZopYGYVEAcyetFNKliRJkiRpxDAclCRJkjQyVaHSlIVqTE8mIlWuEhOwMgpZnIr40YkhudTonFxekiRJknZ9Xu9tK8NBSZIkSSNSewlyJOy7pKN3GNEyCeV0RHZijrfsUxnS+iRJkiRJGo6cc1CSJEnSiDQmB20dxT4XNWlgWqFMqewdpJIkSZIkbY7hoCRJkqQRqRpDqlzttzwZglokSZIkSRopDAclSZIkjUhTm6Fcialu0iS4tCHDzGYjQkmSJEmSNsc5ByVJkiSNSG310Do1w/IVVcIE0tWY1Q1Z1jRnueONhaEuT5IkSZKkYcnOQUmSJEkj1g3vCnjNgRHFuojl4+rYd2bIPacU2at1qCuTJEmSJO1ICcGgfIxGdg5KkiRJGrHq0vCN49ZfzPWff1CSJEmSJPVl56AkSZIkSZIkSZI0ShgOSpIkSZIkSZIkSaOE4aAkSZIkSZIkSZI0SjjnoCRJkiRJkiRJkkaUZKgLGMHsHJQkSZIkSZIkSZJGCcNBSZIkSZIkSZIkaZQwHJQkSZIkSZIkSZJGCcNBSZIkSZIkSZIkaZQwHJQkSZIkSZIkSZJGidRQFyBJkiRJkiRJkiQNREIw1CWMWHYOSpIkSZIkSZIkSaOE4aAkSZIkSZIkSZI0ShgOSpIkSZIkSZIkSaOE4aAkSZIkSZIkSZI0SqSGugBJkiRJkiRJkiRpIJKhLmAEs3NQkiRJkiRJkiRJGiUMByVJkiRJkiRJkqRRwnBQkiRJkiRJkiRJGiWcc1CSJEmSBtnCLviXu9LctTQkBiDo8/jJM8pcdlR1KEqTJEmSJI1ydg5KkiRJ0iB6pgMOuzbHHUtTxITULruCPh//Oz/NK65ND2mdkiRJkqTRyc5BSZIkSRpEp9+YYdNOwf4Cnu2KgPJOqEiSJEmSdj3xi153aUvsHJQkSZKkQbSwZ2svswJ6Kju0FEmSJEmS+jEclCRJkqRBsrATXrxrcIOUN7pKkiRJknYyw0FJkiRJGiT1KYBkq9ff+jUlSZIkSRochoOSJEmSNEiaMgNZOyEb7ahKJEmSJEnavNRQFyBJkiRJu4r24kDWDshXoM6rMkmSJEkasGQAUzqoLzsHJUmSJGmQ1KUHtn7otawkSZIkaSczHJQkSZKkQdI4oHDQYUUlSZIkSTuf4aAkSZIkDZKOIuDQNpIkSZKkYcxwUJIkSZIGSVMGIBnqMiRJkiRJ2iLDQUmSJEkaJMaCkiRJkqThLjXUBUiSJEnSrmJ5DzisqCRJkiTteN6cue3sHJQkSZKkQZKJhroCSZIkSZJemOGgJEmSJA2SWs+g969KkiRJkoYvw0FJkiRJGiSLegIGMqxoJd5xtUiSJEmStDmGg5IkSZI0SA5oTRhI52Bik6EkSZIkaSdLDXUBkiRJkrSrWFUYyNoBpSqknadQkiRJkgYsGcCoLerLzkFJkiRJGiTlAQ4TesRvMjyyasfUIkmSJEnS5hgOSpIkSdIgqR/g2CxLCxFH35DjzP9L75iCJEmSJEnahOGgJEmSJA2Sx1YHMOChbQJ+vyjF+fcYEEqSJEmSdjzDQUmSJEkaLEGyzZte+WRER2kQa5EkSZIkaTMMByVJkiRpkBw8FmDbAsKYgGX5gXYdSpIkSZI0MAOcEUOSJEmStCWProGBDytaE5KwR/O2dx5KkiRJ0miSbOO1l+wclCRJkqRBc+h42J7OwevmeYkmSZIkSdqxvPKUJEmSpEESbecV1n/fnx6cQiRJkiRJ2gLDQUmSJEkaJhZ2Bzzb4dA4kiRJkqQdxzkHJUmSJGkbVJOAT91W5bv/aCRfgYCE6nbPeRHwwMqQ3Zurg1KjJEmSJEmbGvTOweOPP56zzz57sHc74n3uc5/j8MMPH+oydpo4jlm8ePFQlzEoFi1a1Pvv++67j8MPP5w5c+YMYUWSJEkaDubkD+Krf0vorgTEBFQJgWDdx7abPS4elPokSZIkaVeWDNLHaOSwojvJySefzAUXXDDUZewUXV1dnHnmmbtEgPaRj3yEH/zgB0NdhiRJkoahWwt7D/IeE6DKl+4PuHFhwGfujfjWgyFri4P8NJIkSZKkUc1wcCeZPXs2xx133FCXsVN0dHQwd+7coS5jUNx7771DXYIkSZKGqa4kO8h7DICIOQuzvPuWHFc8keG/H8iy91U53nlTisVdg/x0kiRJkqRRyTkHJe0yUg/Pp/4bvyV8fhWlNxxCz0ffSuZPD1B3xc1QqVLZfzrpvz1FtGglSSqChhzxmAaKp76S/FmvJ3PDfdRf+kfC55YRdBUIqjFJUx2V/aYSdBep7jGZnvNPIGmqo+E/f0Hmz49AAMU3H0r3Z06h8V+vJHPbIxDHxM0NVA+eQc/H3kbl0D0ACBevpvGjPyBz7xMQJ1T2nEThPUeT/dMDBB15SkfsQ+rpJYQLVhC3NROu6SZauIIgXyJuaaD7UydSfPfrAAhWdtDwtetI/+kBwtVdkEDlwOmUX3MAmVsfJugqkCQJjGmgMmsCqWeXkTTkKJz2StJ3PU7qkflUXroX3eefSDKuaatf47pvz6H+W78j6CmQ1OcovP3VEAak//YUlX2m0vPxE4hnjO9dP3v1XeSu/DOkIvLvP5bq3lOo/+p1pJ5aQulV+9HzqZNImur6PklXgYZvXE/6jrm9r3l1z8nb/f7YGrlf3E7uezcRLV5NUo2hIUvpqAPp+fTJxFPHbde+g8489V+/nvSdc6nuPZXu808k3n3ioNQdrOyg4avXkbr/aSoHziAe30zmpgcI13aTNOYoHXMw+X8+lrrv3UT63ieo7r8b3f9yPPXf/QPZ6/5KUK4SN9b+wB10FwhKVZJUCJkUlCq1J0kgqFbXNbVsYbi7AJIohLosxDFBoQRxUvsaR2Ht3w1ZkoYc1WltpB54lnBN96C8BjtcAAQBSZIQDPPxJpIAyGYgFRL0FCHeqOCAzY6XMW4Ly7e0/82+BkFQ23/c/8GkMUf3595O4Z9ex5gjPkXqmWW15VFI4YwjST21hPTfn4VqTNKYo+OSsym/5gAaLvot6bseozptHIQh6fueJljRTlCsQABxayNJWzPV3doovfYAsn9+hKC9h8KpR1B437EQBETPLKX+q9cRPTKfaEUHQb5IPKYRAghXdcL693UYkqQjyKWpzphA/uw3UDz1lRsOrytP/de2/RxO3f04TR/7IeHSNcTTx9PxnbOozp4FQO7nt5H75R0kCZCOCNt7KL9ib7o/dRJJa+OGGrbx+0jqoXm1n4+LV1N640voOfcttfNb2+y65yLOvj23E58x4OYlKQ65tvZ1+983FHjVzvnRKEmSJEnaBQVJkmz1n7iOP/54Xvayl3HQQQfxox/9iFWrVrH33nvzoQ99qHc+veOPP57Jkyfzve99D4AkSbj22mv57W9/y7x586hUKkyePJnjjz+e97znPQRB0LvdEUccwSGHHMKPf/xjFi1axMSJE3nHO97B6aefPuADO/vss8lms+y333788pe/JJfLcemll7Lnnnvy7LPP8t3vfpf77ruPcrnMPvvsw1lnncURRxzRZ/tUKsXb3/52Lr74YhYvXsyMGTN4//vfzzHHHNPnuW6++WauvvpqnnjiCYrFIhMmTOCYY47hgx/8IJlMBqjNOXjDDTdw33339X7+8MMP8/a3v53vfve7APz3f/83r3zlK9laXV1dXH755dxyyy2sXbuWadOm8Y53vIMTTzyxd521a9dy2WWXcdttt7F27VomT57M2972Nt797ncTRREAl19+Od///vf57W9/y5QpU3q33XT55Zdfzk9+8hOuuuoqvvGNb/D3v/+dKIp4zWtew8c+9jHGjBnDfffdxznnnNOnzk33+0LWb/+d73yHm266iVtuuYU4jjn66KP51Kc+xf333893v/td5s+fz4wZM/jYxz7GS1/60t7tC4UCP/jBD7jxxhtZsWIF48eP5w1veANnnXUWuVztDzhz5szh85//PL/4xS/4yU9+wt13302lUuFlL3sZn/jEJ5gyZQqLFy/mbW97W5/aLrvsMgDOOeccPvnJTzJv3jxuvvlm8vk8Bx10EJ/4xCfYc889t/rrt6OtWNE51CXsVOGS1bQe+RnCrkLvsuIxs8n+30NbtX3+tCOpu+auF10vbmsmHttI6sm+c2rGTXWEnfl+6yf1WVbf+SXiya20HvZxUovX9H2cgc1K1P7jj1I67jDGvPFzpP/x3AC23PzzlQ/fk7W//8+t2jZ35Z9p+viPXnCd6tSxrL7nK5DLkL36Lpo/8r0+j8fNdYQdG16n4rEH0/GLj/dZp/mfvkX2j3/fsE1bM6v/eiFJ4yYhIrUsoq2tiZUrO9n6n2abl/vZn2n6xOaPrzJrImvu+hKkom3ef/M7vt7n/VidNIbV914I9dvfdTLmDZ8j/cALvx/i5nrCjp4Nn9dnCXscp047V2VKa7/vg5uTAKVX70f2jse2et+bfn/r+s/TKbz3aMa+/NOEK9oHXCtAx6XnUDyl9vth8zu/QfbmB3sfq05cdw43vPg5HM5fwdhXfIpgo2A9yaZY9eC3yM75G02f/PFmtyu9cl/ar/tM7+fb8n0kXLzu52P3hp+P+TOPpuvC97xo3dq8h1YFHHtDlu2dV3D7JCz7pwLBUJYgDYLB/F1OGo08h6Tt4zmknWX8+K2/MV8Dc0cwOFOCvTr550HZz0gy4FuG//KXv/CHP/yBM844g3HjxnHttdfykY98hO985zscdthh/da/9NJL+eEPf8hb3/pWTjrpJLq7u/n973/PJZdcQkNDA6eddlrvunfffTc333xz777/93//lwsvvJApU6bwqle9asAH98ADD7Bo0SLOO+88nn/+eWbNmsXTTz/N+9//fsaNG8d73/teUqkUN954I+eddx5f/OIXecMb3tC7/bPPPsunP/1p3va2t3HKKadwww038OlPf5ovfvGLvOlNbwLguuuu44tf/CKvec1rOPfcc6lUKtxyyy387Gc/A+C8887bYn1Lly7liiuu4Oyzz2bFihUcdNBBW31s5XKZs88+m2eeeYaTTjqJvfbai7vuuosvfvGLFAoF3v72t9PR0cH73vc+lixZwimnnMKMGTO49957ueSSS3jiiSf40pe+NODXtFqtcs4553DIIYdw3nnnMXfuXK6//nqKxSJf/vKXmTVrFh//+Mf5xje+wete9zpe97rX0draOuDn+fznP8+sWbP4yEc+wv3338+cOXNYtmwZTzzxBGeccQZNTU38+Mc/5tOf/jTXX389TU1NlMtlPvShD/Hwww9z/PHHs//++/PII4/wk5/8hAceeIDLL7+cVGrDW/7jH/84u+++Ox/+8IdZtGgRv/zlL1mxYgU//elPaW1t5YILLuCzn/0sL3nJSzjppJOYNWsWzz1X++P7xRdfzF577cUHPvABli9fzs9//nM+8pGPcN111/WGkMPBaPqDUfb6v/YJBgEytz681dvnbvjbVq0XruwgXNnRf/lmgkGAoKdI7vq/UDl45mb/ID7QL1H9pX8gnjF+m4LBzT1f+r6nST2xiOq+015027rLbnzRdaLnV5O99WFKxx1G3ZV/7vf4xsEgQPbmBwmXrSWZNKZW38oOMjf+o+82KzvI3viPPh08661/jw/Gez23mXrXSz23jMzdj1N+7QHbtO9wyep+QXW0dC3Z/3uI0tteuoWttk700PwXDQaBPsEgYDCoIRFtRTAIte9VAwkG12+zsborbyOZOnabg0GodfSVTj2CcOmaPsEgQLRsLdmbH6B04stfdD91v7y9TzAIEBQrZH9/3wt+78nc/TjRvGXEsybWatiG7yO56+7tEwwC5K66g+4vv7vW0asBu2FBxNAGgwAB188LOWn3LXRySyPEYP4uJ41GnkPS9vEckjSaDTgcXLp0KV/72tc46qijAHjLW97CySefzCWXXMKPftS346JSqfCrX/2KN7zhDXzuc5/rXX7iiSfyhje8gbvvvrtPOLhs2TJ+8YtfsNdeewFw1FFH8eY3v5k//vGP2xQO5vN5vvCFL3DggQf2LrvwwgtpbW3l5z//OXV1tS6UM844gw9+8IN8/etf53Wvex3pdBqAlStX8vGPf5x3vvOdvXW/4x3v4Fvf+hZveMMbCMOQK6+8ktmzZ/P1r3+9twvy1FNP5YQTTuCee+55wXCwWCzyX//1X30Cya11/fXX8+STT/YJKk8++WTOPvtsfvSjH3H66afzk5/8hAULFvT5ep122ml85Stf4ZprruEtb3nLgF/XarXK61//ej72sY8BcMopp7BixQpuvfVWCoUC48aN46ijjuIb3/gGe+655zbPs9jW1sbFF19MGIacdNJJ3H///fz1r3/l29/+dm93ZV1dHV/84hd59NFHecUrXsH111/PQw891Odrduqpp7L77rvz7W9/m9/85jd93m/77bcfX/3qV3s/z+fzXHvttSxYsIDp06dz3HHH8dnPfpapU6f2Hsf6cHDWrFl8//vf7w0b0+k0l19+OQ8//HCfTsah1NpaT2o7upxGnDH1/RYFYbDZ4e02J9iBfyBtaG2AtsG5QyjdkKV1QvOg7Gu91gktW1dfLr1V+2se21jb39Z0xIUh4yZt9PwhEAZQ7ft1axrbSNML1DhuAEOjblFd5gUfbmlr2vavY6VSu9rY5FbE5nGN2//emODdZxo5duY1d5RL0zS28cVXfAGZ+ixtbU0QVyEMIe4bxGz1ObyZn1FQ+972Yt97xk4aU3uOanXbvo+Maei3KEhFtI1vqh2TBmzGuBgY+lCuqTlHW9so+l1Pu7RB+V1OGsU8h6Tt4zkkjVzJkN+4OXINOBycOXNmb9AE0NraynHHHcfVV1/N6tWr++48leKmm26iUqn0Wb527VoaGhrI5/t2kMyYMaM3GIRaQDR27FhWrVo10DIByGaz7L///n2e9+9//ztnnHEGxWKRYnFD18RRRx3FRRddxKOPPsohhxwCQGNjY58wKZfLccopp/DNb36Txx57jAMOOICrrrqKfD7fGwwCrFmzhqampn7HtzmHHnroNh3bHXfcQWtrK2984xt7lwVBwAUXXEClUiEIAm6//XZmzZrV5+sF8P73v59rrrmG2267bZtC19e//vV9Pt977725++67Wbt2LZMmTdqm49nUa1/7WsJ1f7AKw5Bp06bR1dXVZ9jV9UOVrly5EoDbb7+dhoaGfsPQvuMd7+CKK67gtttu6/P13NxxAKxatYrp06e/YH3HHHNMny7E9e+zbX2v7ghr1vSMqjufgtcfQmtbE+HKDcOpFt76UrJz/tavW2Nzet7xKup+8H8ELzKORHW3NuLJraT/+lTf5eOaiFb1H8o1HtfEmje+hGRcE2P2nEzq6SV9Hh/IsKIJ0PHBN1OeMIbm1+xP5va5W7nlRvvYZK6w0lEH0jG2CVa++DC0mY+9jab3XvyC9VZnTWTNoXvAyk4y73kdzbds6N5MopB4fDPR0rW9ywqnvIIugj7P33jKK8hdffeGfe7Wxpoj9t1sjUFQ+yV+1artHwIkc+bRNN/9xGYfqxwwnbX7Tduq12mzUimaTngZ2ev+0ruoOnMCa16617bvc71JY2l+1X5k7nzhLqtqWzPRRl2v8Zh6wrU9L7CFNPjKe00m/dSSF10vCaD0+kPI3vTAVu+737Ci7z2awiv2oXV6G9GClQOuNQkCOv/pKEorOyGMaDrhpWR/s8k5/LK9t+ocDk96Ba1f/DVBacPvxHFjjjWvPYBMoUzTvU9udrviWw6jsy5be44o2qbvI8EbXkLruKba/Irr9Lz3aHpWj5D5Poeht0yCj5FjqIcVPWZcnpUDf2tLw8pg/i4njUaeQ9L28RzSztI2SE0D0mAacDg4a9asfst22203kiRhyZL+f+xJp9Pceeed3HbbbcyfP5+FCxfS0VH742S8yd3Xmxt+MpPJUK1WB1omAGPGjOkNmAAWLVoEwK9+9St+9atfbXabpUuX9v572rRpvV2E660PjZYsWcIBBxxAKpVi7ty53HjjjcybN49Fixb1hqSTJ09+0Rq3ZcjN9c8/bdq0PqHkps+5ePHiPvMortfW1kZTU1OfYx2IMWPG9Pl8/Wu06ddze4wbN67P51EU9Xut1n9t10+buXjxYqZOndontFtf39SpU/u9Pzfd3/r5Ibfm/bbpttlsrUOqXC6/6LY702j6xSYZ18yaG/6T+kv/QPj8akqvP5jCe15H4cyjyf34FoJKlfLhe5C++wmiJ54nqctAUx3x+BYKp76S0lsPp3TsIdR9/09ETy8h6OghKFeJJ7VSPmx3ouXtVPacTP7DbyZpqqPuG3PIzfkrBAGFE19Oz0ffQsMFV5P93X0EcUJlcivVl+5FzzlvJB5X6/Rbe8N/0PC5X5L9/f1QiSm/ZBaF9x1D9o//IOjMUzrqQKKnlxItWEHc2ki4ZA2pecsIugpUJ7TQ/R+nU37tgZBAx48/St2lfyRz4z+Inq99zym9bC9Kbz6U7J8eJOjKkySQjGmguuckUo8sJGnMUTjjVWTueJTUIwsoH74nPee8qfYX9a1QfMvhJJedQ8N//5pwVQdJWws9Z7+esKtA+p4nqOw7lfxH3kKSTkECxTcfRvsvP07u57eTpCIKZx5NZY9J1F/6R6KnFlN+9f7k33dsv+fv/Mb7qew/ncztj254zXOZF6wzSbb//V488RW012Wp+/6fCOcth2oMLfWUjp1Nz4eOIwnCrX6tNqfj4rOoO2gGmbseo7L31NpxrXuttlfHT8+j7rt/JP33ZyjPnkl1tzayv7uPcMkaaMhSOno2Pe89hvqf/Zn0vU9Q2W83ej74RnI/u426n98GxTJxSz1BJSbIFwl6SiS5NEQRQU+RpC4NCQTdxVrnVLlCUNnoe364rpspDEmyKZKmOogTgq4CkJC0NpFEIUG1StzaSDK2ierMCaT//AjRopUbUp31r8XG/waSMKgF90P4PS3JpAiCgCSJCcrVra5l00B+QM+5jdsm2RRk0yTpiKCzsCGQCiAJw/7DW+ZSVOOAsFTuXW/98SVhbUEQJ7XtM2moVAjWd/cGtZWTIKg1tYVh7fXZRDyhhe7PnkHx9CNpfsfXyNzyMCSQ5NIU3vEaovnLydz9OJQrxBPG0P6DD1E9aCZ137uJzB1zqcyaAA05Un97muiZJbVgOwypTm0lnjGB6vQ2SkcdRPbGfxB09FA8+QiK64b7XDvn36n77h9IPbKAcOEqws481QktBOUK4dK1tXM9CknqMrWfDdk01d0nkX//sZSPPqj3tej49lnUHTSTzJ1zqew1hfxHjqu9HlvxNapObGXNzRfQ+IkfEj23nMr+0+j61j8TN9RROPkI4vosuavuJAkCkroMYXs35VfsQ/6s1/fZ/7Z8H0namln7u/+g7rt/JFy8mtIbX0Lh3a8d0vNppBuTgcfOKLDfr9bPO7gzQ8LaF+6Ot9WGih1Nv+tp1zYYv8tJo5nnkLR9PIckjUYDDgc3DctgQygURX2HtUmShE984hPccccdHHLIIcyePZuTTz6ZQw89lHPOOafffjYNurZXuMlQSevrPO200/p10623xx579P77hY51/b4vvPBCrr76avbZZx9mz57Ncccdx8EHH8yFF164VeHbpq/Z1qpWqy/6eiUv8FMtjuN+Idrm1tmcTV/XHWFzr8v2HG+SJP2+ntvzftsZr4EGLt59Il1fPbPPsvIr96X8yn17P89/aMtD3ZZfdxDl123d3J89nz2dns/27VLtvvA9dF/4ni1uk4xtpOvbZ9H17bP6LC8d/7Ktes4++2qso+eTJ9HzyZP6PVZ8+6tfcNttnTcPoHTyEZRO7n/TwRbXP+ZgSscc3GdZ9+fe/sIbZVLkP/Rm8h9687aUuF1Kb3wJpTe+ZMfsPJsmf+5byJ/7lkHfddJYR8+n+r4Xiu8+qt96Pf9yPHB87+f5T5xA/hMnDHo9GjmCoHYH45qVO+9O2Y5fnr/V6+Y/+lbyH33rVq9fOr7/0N7x5LF0f+H/bfU+tiibJv+R48h/ZNuGTK/uO5X23/3nZh8rvelQSm/aitEktvH7SHX3SXR97cwBbaMXNi4Hi/9fN9N/nqPC1g27PTAb98ImpAK47fgCe23bfYWSJEmSJPUx4HBwfffdxhYuXEgURb3DPK73j3/8gzvuuIN//ud/7hMGVioV2tvbmTp16jaUvO3W15dKpXj5y1/e57Fnn32WxYsXk8vlepctXryYJEn6hEgLFiwAah2ES5Ys4eqrr+a4447jggsu6LO/HT285KRJk3j66af7Lb/rrru46aab+OhHP8rkyZOZP39+v3VWrlxJd3d37xCg64O4UqnUZ73hNETm1pgyZQoPPfQQlUqlT/BZLpdZvHhx73CxkiRJ0mA4Ovs4NxW37saigXjlhCpfO6LCTc+HvGFqlT3HDPpTSJIkSZJGsQG3P82dO5eHH94wh9SqVav4/e9/z+GHH05zc3Ofddvb24H+Q5Fed911FAqFbR4udFu1tbWx//77M2fOHFasWNG7vFKpcMEFF/DpT3+6z/yIq1at4k9/+lPv5/l8nmuvvZbp06ez55579h7f7rvv3ud57rzzThYsWLBDj+/II49k1apV3HrrrX2W/+IXv+DOO+9kzJgxvOY1r+G5557jz3/+c591fvKTnwD0zje4fgjPJ5/cMN9Nd3c3d9111zbVtulwnzvLq1/9arq7u7n66qv7LL/mmmvo7u7m1a9+4W6qzQnDcFCHS5UkSdKu4/SGB2gY8O2WLybhQwdU2HNMwocOMBiUJEmSpC1JCAblYzQa8KVsJpPhox/9KO985zvJZrNcc801JEnCv/zLv/Rbd/bs2TQ0NPCNb3yDJUuW0NzczH333cef/vQnstksPT09g3EMA3L++efzwQ9+kHe9612cdtpptLS0cOONN/LII4/wkY98pM98eqlUis9//vM8/vjjjB8/nt/+9rcsW7aMb37zm0AtFJw0aRI/+tGPKBaLTJw4kUcffZQ5c+aQzWbp7u7eYcdx8skn89vf/pZ/+7d/47TTTmPGjBnceeed/OUvf+Gzn/0sURRx5plncsstt/CZz3yGU089lenTp/PXv/6VW2+9lde97nUceeSRABx11FF87Wtf48ILL2TJkiVkMhl+85vfUFdXt021rZ/r8bbbbmPSpEkcffTR/YLjHeHEE0/khhtu4KKLLuLpp59m//33Z+7cucyZM4eDDjqIE088ccD7bG1t5f777+c3v/nNZudvlCRJ0ugVBrDonIhTri3w5yUpNpnRcxv3GrCqFAI790ZKSZIkSdLoMeBw8MADD+SNb3wjV1xxBV1dXRxyyCGce+657LXXXv3WHTduHN/61re4+OKL+eEPf0g6nWbGjBn8z//8D4888ghXXXUVq1at6u1c2xlmz57NFVdcweWXX86VV15JpVJhxowZfO5zn+Otb+07p8348eP5xCc+wTe/+U1WrlzJfvvtx3e/+10OPbQ2J0wmk+Fb3/oWF110EVdddRVJkjBt2jTOP/98KpUKX/va13jsscfYb7/9Bv04crkcl19+OZdeeik33ngjXV1dzJo1iy9/+csce+yxALS0tPDDH/6QSy+9lJtuuonOzk6mTp3Keeedxzvf+c7efbW2tvKtb32LSy65hMsvv5wxY8Zw0kknMWPGDP7t3/5tm2r70Ic+xM9+9jO++tWvMm3aNA4//PBBO/YtyWQyXHrppXz/+9/nT3/6E3/4wx+YMGEC733ve3nf+973onMsbs65557LxRdfzFe/+lX+/d//nYkTJ+6AyiVJkjRSjckF/PyYAlG04XfNCT/JvcAWL+6x1aPzzlVJkiRJ0s4RJAMY+/H4449n8uTJfO9739uRNQ0LZ599NkuWLGHOnDlDXYq0TVas6BzqEqQdLgigra2JlSs72ckjGUu7BM8hadtVKhX++MfreO9738vSpWt6w8F8BWb8PMe2dw7CP+9b5n9eXnnxFaURzp9D0vbxHJK2j+eQdpbx45uGuoRd1m3BDwdlP69N3jco+xlJBjznoCRJkiRp8+Z3BmxPMAjw0vHOeS1JkiRJ2nEGPs7iECmXy7S3t2/Vui0tLaTT6R1c0eBbuXLlVq1XX19PfX39Dq5m8LS3t1Mul190vXQ6TUtLy06oSJIkSdoxptYnQMK2B4QJb5tpOChJkiRJ2nFGTDj44IMPcs4552zVupdddtlOmeNusL3pTW/aqvXOOussPvCBD+zgagbPJz/5Sf7+97+/6HqHHnroqBiyVpIkSbuuZYXt7xx8vidgeqNjW0mSJEnSC/GqadsNKBwcyvn39t57b77zne9s9brbayhCqq09vqlTp+7gSgbXxz72MTo6Ol50vebm5p1QjSRJkrTjtOW2t3Mw4JbnQ87cpzqIVUmSJEmStMGI6Rxsbm7m5S9/+VCXsUPtqse33377DXUJkiRJ0k4xJrv9+5jW4P2vkiRJkqQdJxzqAiRJkiRpV1HezukCX9JW5XVTnHNQkiRJkrTjjJjOQUmSJEka7uauHsicgwmvn1Jh39aE53tCjp0Wc8LMKpG3cEqSJEmSdiDDQUmSJEkaJHUDvMK68tgKQQDgHIOSJEmSpJ3DcFCSJEmSBkmhMtQVSJIkSdLokGz1qC3alAPWSJIkSdIgSUiAZCvXDoi3dlVJkiRJkgaJ4aAkSZIkDZKmzEDmHIS8nYaSJEmSpJ3McFCSJEmSBsnMpoF0DrJuvkFJkiRJknYew0FJkiRJGhIJDemhrkGSJEmSNNqkhroASZIkSdpVLO2BgQwrWokh5S2bkiRJkjRgTuG+7bwMlSRJkqRBMr4OBnKJ6sWsJEmSJGlnMxyUJEmSpEGSHtAVVkA53lGVSJIkSZK0eYaDkiRJkjRIqgMK+xJy0Y6qRJIkSZKkzTMclCRJkqRBsrYIA5lzMNz6VSVJkiRJGhSGg5IkSZI0SDrLA1s/dtJBSZIkSdJOlhrqAiRJkiRpVzGjCSBh67oHA3rK0JjZsTVJkiRJ0q4oGcCoLerLzkFJkiRJGiTBgK5NE4NBSZIkSdJOZzgoSZIkSUOkEg91BZIkSZKk0cZwUJIkSZKGSOQoOJIkSZKkncxwUJIkSZIGUS5MtnrdgQ1DKkmSJEnS9jMclCRJkqRBdPfJReDFAsKEn72uuDPKkSRJkqRdUkIwKB+jkeGgJEmSJA2iaQ3wzDsKvHR8hVwYExJTCwtr/x+XqjL39AJvnL71HYaSJEmSJA2W1FAXIEmSJEm7mqYM/O648lCXIUmSJElSP3YOSpIkSZIkSZIkSaOE4aAkSZIkSZIkSZI0ShgOSpIkSZIkSZIkSaOEcw5KkiRJkiRJkiRpRImHuoARzM5BSZIkSZIkSZIkaZQwHJQkSZIkSZIkSZJGCcNBSZIkSZIkSZIkaZQwHJQkSZIkSZIkSZJGidRQFyBJkiRJkiRJkiQNRBIGQ13CiGXnoCRJkiRJkiRJkjRKGA5KkiRJkiRJkiRJo4ThoCRJkiRJkiRJkjRKGA5KkiRJkiRJkiRJo4ThoCRJkiRJkiRJkjRKpIa6AEmSJEmSJEmSJGkgkmCoKxi57ByUJEmSJEmSJEmSRgnDQUmSJEmSJEmSJGmUMByUJEmSJEmSJEmSRgnnHJQkSZKkESJOEk75WcK9qyMC4NQ9qnzzrSGhc21IkiRJkraS4aAkSZKkXUpciQlTu+YgKa/+bpWnKllYFwZe9WxI+LsS33xrNLSFSZIkSdJOlniX5DYzHJQkSZK0S3j6mgU89J2nSWIIUwFH/PeBTDpi/FCX1euL1xf53cMx6QBOPjjivLekCYKtv5hd1Z2wKB+RC6rUVaqUwpDudMS1T0R88607sHBJkiRJ0i7FcFCSJEnSiNe9uIcHL3kaggBCiGO469MPccKfjiKVHfquurd/q4dbu9MkUa2Wrz8YU4rLfOptma3ex9o8tJQqNFSqvct6yiEd6RROJy9JkiRJ2lpeQUqSJEka8e75xD9qweDGwpCHLnp8aArayMJlVe7qTnFYZw//b+kq3r5sFXv1FLj6geqLb7yRllxCfaXvNvWVmNZCeTDLlSRJkiTt4uwclCRJkjTilZ/qhMZs34VJwpoHV23b/gox9/xkMauvfY50V4n6g8dw2Gf2pXl6w4D3dfM/yuzbXeKwrp7eZUd2dHNPSwNQt9X7eXx571SDlIOABMgkCT0vtJEkSZIkSZuwc1CSJEnSiNfQWSZV7ttVl81XSFYUt2l/v/2febT/8ElWjGlh4YxJPLk6w7UfeJhK19Z36bWvqXLzdWuZe283e+X71zGzUBpQTXtNDCgAHamIGEiA9lTEWq/qJEmSJEkDYOegJEmSpJEvgZa1BfJ1aSqpkEy5Sq6nTCkeWHKW+t97WfRfdzAu2p3F06Zz0KMLSIBndp/M2nFj+Op58/jMFXu96H6enFvgqouXk4oTxgL1qRRx2LeW6qbDoL6IiQ0B+VRIYzXuXVZfjamkTAclSZIkjT6Jl0LbzJdOkiRJ0ohXzQSECTT0lGnpKFKXrxAEAS0dW9edl1Riwr8/x9z/uI+/Nh7C8nETeen9T9PYXeDvh+zBqnHNVFMR9ZWE7/zP8y+6v19/byWpOOkdBjRdrUKS9D5eCeCvzQMfojQb91+Wqyb9F0qSJEmStAV2DkqSJEka8XJJlTLRhgAuCAirMUF5M2naRpJKzLLXXU/nM90E9Z083lbrCpy8ci2paswT0ydQSW902RQEFJ7soXPVlocXzffEVPPVPhdbUZKQq1RYkctRTEWsSafpTA/scmx5d0JE/yBwYP2HkiRJkqTRznBQkiRJ0oiX6a6QK5aJqkAApUxIqhLT1fLClzwLX/971i6sMH+3CSThRACylTIHrXicNUwmn0nTnstSjiKylQqNxRLpasz9X3wA3rj5fVaKMdUwJIrj3uCuEsCt41qY29RACEwrVYiqLxxcbioKA9LVmHIY9g4BEwNJYuegJEmSJGnrOayoJEmSpBEvKEOqWuuiCxLIFmNIoDQ284Lb5R/rZOH4ZpJwQ/9dMZWmszlDHCQ8MWMS7fV19GQzrGmoZ1VDPXX5Ium5Wx5aNA4C4jBkVV2W8rr93t3awiPNjcRBQCUImJdN0zPAOQebM7AsFVEFwnUdhD0BrHHOQUmSJEnSANg5KEmSJGnEiyr9u+cqqZDsxPoX3K4YRVQ3E67dtvvLaG9uYk1LY5/lPZk03fUZ5pemMJbFm93nwi64eUYzxXQD0zt6GN9dZG5L//kFNw4kt0YMVMKQJamgloKuO+SUnYOSJEmSRqEkcpKFbeUtppIkSZJGvOpmgraonJDtqbzgdvlMimQz8/iVchm6G3L9NwgCAiCfaaC4ejOPA++6vkpdnOWIxWuZ2lUikwQctaaLYJMQr6XywrVtqhpDKWBde2QAYS0k3EwuKkmSJEnSFhkOSpIkSRrxKumwT8SXANVUSPuCblbduPkOP4DuxhTpckwQJ9R3lRm3LE/L6hINnSVypRKpSrXP+k1d3YxfvpogCMjc0H8gljueD1ieaWTv1V19t4sTJpfKfZaNKeUHdIxPLq2uCwQ3CkLXh4SSJEmSJG0lhxWVJEmSNOJVUwHFTERUrUWE1agWogVJwH3/8RBvfOOUzW5XV6ySBDExMVE1IYwTUnGViYtXMm7FGsZOaufpmVMoplM0d3Sz57znSVWr1FUK1HVV++3vjwsC6soVos108x3W2cPi5noggHTEsnBgLX8rO2IIov4PGA5KkiRJkgbAcFCSJEnSiFdKQVQKqKY2CsqShO6mLLnSlofvbO4usrytkTiqDaqSrS9TX6yFfqlqzLTnV5GpVlkzrpkgjokqVcYWOpm2ahUL6yb2299bJpb5RZJmQWOOSiqkuVihLV+CJKGpWmVypcqSdcOVRtHALsfySUAK6Hc0hoOSJEmSpAEwHJQkSZI04mWKCUEM5WzEqklNlLJpGtfkqespU82EJKUqQaZ/193zE5p7g0GATCXut05dd5GlU7IALJk5mUnPdjBt+UpWZ8b0W7cnFdGdS3PvjLG9w39O7sjzkkWrmVKsMCFf7A0HW8oVtvaSrBon/PB3JaIkQyXcaHaIZP1/DAglSZIkSVvHcFCSJEnSiJcpxZSjiOf2m0Q5V7vM6RxbT8PaHlpWd7Dyv/7O+C+9tN92xWzfS6I4CIC+w32W0xvWqaZSLJgwmSPmz2XZpIZ++/vTogiivvMCLmmuY990SGOpxNTOHh4c1wzVmGyxzAtdki19vsSFVxf4e1hPJheQ6o4J6vqVR3OpAmS2uB9JkiRJ2hXFjqKyzcIXX0WSJEmShrdSJmTNpMbeYHC97pY6Wjt7ePz3yza7XVTt2ylYyKX6ZG/VMGDlhNY+6yRhQBKW6JxW7Le/MRn6BIO920QRCVAOIyas7YZSleejDZ2Ma4tQ3qiU++/q5KPfK3Dl2Ik821BP05I844olWioVJpfLTCxXCJNapdPyReJ4YPMXSpIkSZJGLzsHJUmSJI1o1VJMSLIhlEsSMsUKcRRQSUVkitBQ7NrstplSlTgMKafD2vYBxCREBNT3lHh2j8kU6rN9thnfsZZrXvJ6MqtX99tfV6n2/BsHhEGSMKUzT0c2y8p0ilSpAqkUpUya+54p866bM6zO5YjimDP3LvOlI2P+8PPVNLQ08c/3PVM7pCCgEgQcFEYEQUAC9IQB9zXWkU9H/boJJUmSJEnaEsNBSZIkSSPS3V95nIX/t4Ikgd1SES2re1jT1sDExWvJliokQCGXpidKMymfp9peImrJ8LfvPcdzNywlqSZMLFdprsQUsilIEnL5mPrOEnEuprWzzOyH53PzxNbeKf2iuMK8sRMJgYQcK9eU+NEvF1H52wJO7ZlHcuIpELTRUKrQnUmRqVQ5YuEqmksVVmbSlFMRTcDYYpnVmRSn3FJHPpcGoBqGXPFUhldPKVAmZLfOPNWgNkRpAGSShCn5Asvq6wBojBOml8p0RBFh5HA6kiRJkqStYzgoSZIkacS57d8eYcU9K0lRa5rras7SuqrAbs+t7G3aC4C6Qpk4FdCRS5MUK1x/1iP0LCoQJAmpcpXlU1rJ5cu0LusmW6gSJgAh47vXUKGedDXmtY/fTXdTju5MPbOff4Snx+3JE+P3oRpE7H1xnjX1+8Gh+/HFSoULfngtwVtO48S5C0nCkPpSldS64T/XZGohYBoYmyRUy1Xa1y3rFQT8610pTgxDEoJ+Q5Rm45h0tUo5igiAtnKVeQ1e1kmSJEmStp5XkZIkSZJGlGo5Ztm9K4mzaaqpiCBO6IoT6rtKvR1+G6tkQspBxILbV9C1uEiQJETVmDisTcFeqM+wfFrE9KfW9G6TCUpUqGftmBz373sAXdlGAB6cdhBvfvRPRHGFVQ0ha+rq+eDdN3LaQ/eyoqGJnx56LAcsW8u4fIlKGBKvC/fiAFbUp6kGAVGcECUJhdbcZo9vWSmsJZ5baAbceHGUJDRUHVNUkiRJ0uiThENdwchlOChJkiRpRFn9RCfVTJo4XbucSaKAQmOOzjEl6vMVUpuEZaVsyLQ1eZ64bS1JAmEQsGnyVk1HFOtS5PIVgiSmLV7FfeN2Y94+Y3uDwfVu3/OVtHR3csteB/LZP/2az//pmt7H3vrY3znt//0XAZCKYxLgofHNLGuoJwkCljdmqc+XacmXKTb2nctwvWTdEKEJ9Ju/sBSGlNaFmglQDgKC0CFFJUmSJElbz3BQkiRJ0oiRxAn3XfYccSrqszxdqhIAxUxEmK+w/gbSKpDtqlAkYsIfnmHV3pNJNhmqM5/LUqjLkYoT9nluPvt1P8mfZxzCgulTWNXc3K+G7kw94xau5s2PPcEH772pz2P15RJ7rFnKyqbdSIKAVbkMZVK0dZeoAnG+zPK6DOVM1C/421iQxARBxLoRSYkDmNdUx/K6HA2VmM50REO5yuJURHkL+5AkSZIkaXMMByVJkiSNGEvuX0v70z2QCgnimKhUIViXoFUDCIKEfC4kU4pp7cyTqcSsaKknbgx4dO8ppIJaIBeHAWGc0NVYT2dLEwAL6yexbOJY8v/IsXL8ZHrq6qiGAZv2GaarBc547lra5raz6YCe9007gGp2Mi2lCgBjimUeHN9CtlDlsVyG7iiCckwhgLASE6cjNhVUYp5rbGBisUw6jomAB8c2saC5jiCBhxtyEASMzZfIdRZJxQ4rKkmSJEnaeoaDkiRJkkaMp363tPaPSkyqUukT2pVyaeJSBYKAYg66G9LsuWANjcUyc/eYTrZSoZJJkQQBcToirMZ0N9b32X8pk+FvL9mP9a2H6XJ1XZgYkgQQxgmvePY+2vLtQP9pAa+bfUy/mvdfuZp/ueUKyiF8+TXv5Jlx0+hORzyXJP+fvfuOs6K+9z/++s6cvr3Qe7NiRxE0iqiIEVvsudHE2KM3/mKKXmNu1Hi9qRoVW7xRr9GoiTdqTDARxYLB3rCABpDOwvZ26sx8f3+c5cCyq4EFBJb38/HYR9yZ73zme0522J3zmc/3Q2r9gX4AjWlCQcDyeJT3ixNkDAxLZdiroYUJNQ0A1MYiPD56IG0Rl5PW1DOzTwVNdVBeHe75GysiIiIiIiI7DbVrFBERERGRHUbL8o50WkdFX4G1uJ7faZnOwHWoK48Ty3iF/n3WmPwYxyEIuQRO11uiwHFwggCAkA1wrSXs+0Q8n1AQsLDPmC7HrC6qYH6fYdSU9Omyb7c1y9ilbik/Pup8Pq0egmMMJV7ArqtaC1WPBVmfnGf5IB5lRTRMXSRMwvMLlYgAfdJZDlzdyKC2NEV+QN9UmlxW1YMiIiIiIiKycZQclC4uvPBCjj/++C0W79prr2XcuHFbLJ6IiIiI9E7ZxW2s+e/3WXLZa9Q/u4rUqlSXMeUDouRch8DtZjnObvJjfsihOEhx0OIFOIbOPf6MIZzNdRpvgQBwvYBILk0sl+4Ss7s03BN7H8Oj+59IWcqD9RN+1nLkx7P5oN8IakqrOh0T9QOK0+uSfqQ9CLv5L8ehPOcxobmNYdksntO5RnFkcztfWlEHQJVv6TMw0s2sREREREREei/rmC3ytTPSsqIiIiIiIrLNtT2/iiVnvEgmFMZYaPnDUnJhh0yfGPvOOIL4wPzyn8msj3UdMAYv5BLy/HwAYwiMwdkgMdenuY0+fjNuc0DY+mTWP2kQUFXfgMGSjkVpKS0hFw7jWkt5uon2WBG+cXACn8BZl4xcFQvhGYeQzVcX/mPE/vxjVP5huFguR1kqTSYcIue6jKhfxj4r5/Fpef8ur9kzhtza+9BUDjIBhPLnKcvmOL6+ibAFMKQiEWLZfA9CAGscmmJRQkGKrOvS0h7w0ocef1tuGNXX4eLxDvHwznmTKyIiIiIiIp9PyUEREREREflCtK3JkG7KkWr1eOd3y0nVZzF+QLg9y7C3V5IuTcDapzYDixMEOM053p38LHt8Kc7C11po61ONKU5ggVw0gjU5HGsJHEMu5FLUksov1Wkto+prGZWpLZx/aF0dHw0Zmv/GWqLpDGu7BsaTGXZZvZCl/QfTEi2htnTd8qDGWgY2rcI3AR9UVvJ+vyH89yGnMG3hXJ7cZworygbkqwWNIRUKQTTcEdewuGoIiyoHMbJhBZMWvcMLI/fDN7CwPEFjLJ8IddMeZS0ZGiLregbu1prsSAyukw25hLMBadehKRrBGsOKojjD2tq5+FqPl/qWk41FYCX89D2IxgzpdH4qRw0N+K/DLCNKLStaLI+9GzCgFE7bx8GYTU8irmqHFUnD3pWWSNciThEREREREdmOKTkoIiIiIiJblQ0sr96xmAXP1mEDi3Wcdct7WgiSPk2lcWK5YN1BjsF3XDBQnElinlzGbgTsUrOSjwYP4pOBAwmMIQi565b5dKGtqoTihiQjF69hdK620zx2X7Ec34UF/QbhZj3o3LWQZSWDOebd53li/6mYANwgwHddspEwcS/NN974fafxbw/ci7NefZnBTXXUFpfx0PgjeGX4aOrj0fx0goC+yQy3TLqQ6jUfMrh2IeWVfXll+Bha4pG1Lx+f/PKn6wtt2IsQyDkOy4uLyIZcYp5HUSYLQE08Rjoc5pCmJKsTHh8NLMM6hjRAwkLK59mlLs/+weGAao+695PEg3z8u2ca/nRZmLL4xnec+NEbYe6Z5xJYQ9+45X+PyHBAH/U8FBERERER2VGo5+AXoKWlhWuvvZbjjjuOCRMmcOKJJzJ9+nQymfyiRhdeeCHf+ta3eOmllzjttNM45JBD+OpXv8pzzz3XJdaiRYv43ve+x6RJkzjkkEP45je/ySuvvNLjca+99hrf/OY3OfTQQznxxBN54oknNuu1zps3j0svvZTDDjuMY489lvvuuw/bzQcbb7zxBt/+9rc58sgjGT9+PMceeyz/9V//RWtrKwCvvPIK48aN449//GOXY//jP/6DY445Bt/3N2lu8+fP5/vf/z5Tpkxh/PjxHH300fzwhz9k9erVAHzwwQeMGzeOBx98sMux1157LYcddhjpdL7nTG1tLT/60Y846qijOPzww/nRj37ECy+8wLhx43jzzTc3aV4iIiIivd2y15pYMLMOLFhjuvT9y8WipEuihU2RnEd5W4rK1iSJVJYh7fWEWJc4rC8uyf+H0/3tTFFLhvZQlKXxKtLOumq8pnARA1pWcPT8l6hqbOpynLEBRX4bjg/RbI6Q5xPNZIkn0xRl2juNtcComiYGN+X7/vVpa+biF/9KPLtu4VLfcViViNIQj/PxsAP53YEn83H1ENpiHc9oGgp3ZO4GfS4WdFRHri8VcsmGXCK+T3k2R9hawtYyJJ2hXyaLC6yuiK+rvoT8f69dXtQLWLbIKyQGAXJJy41/3fi/q/9R43D3RyECm4+5JmW4Yo76HYqIiIiIiOxIVDn4Bbjqqqv4+OOPOeuss6iurmbu3Lncf//9NDc388Mf/hDIJ/OuvPJKTjjhBE455RT+8pe/cOWVV3LDDTcwdepUABYsWMB5551HVVUV5557LqFQiL///e9cfvnl3HDDDUyZMmWTxr322mt8+9vfZtiwYVxyySU0NTXxy1/+EmMM5eXlm/w6Fy5cyIUXXkhpaSnnnXceuVyOBx98kFwu12ncq6++yre//W322WcfLrroIhzH4dVXX+Xxxx+ntbWVn/70pxx00EFUVlYyc+ZMTjvttMKxqVSK2bNnc9JJJ+G6G79+0dr3ZOjQoXzjG98gFovx3nvvMWPGDJYtW8YDDzzA2LFjGTx4MM8++yxf+9rXCsfmcjleeOEFDj/8cGKxGO3t7VxwwQXU1dVx1llnUV5ezpNPPsmcOXM2+T3b2nqwQpTIDmXtz7h+1kV6RteQfFHqPm5b9013P3AG2oujlDekCHk+Ze3pQk1f2M+SI0KMFADLqqpoLCrCC4XAGKwxuJ6XXxrTWkrrU8SyOXKJEAtL+vNBfAgl2RTD6mv5uLwvQcffkL5j8rHddYmtUc2fsrJkMGaDisKQ7zOsdjkWw6p4X+qjFdSWlzN+8YrCGIcMpV4jl7/0CPeOn0ZNaTUA7a7LkJyPAfZsaWdMKERbSZS60ji4a6snLSWtHqNbMvyzOIbFUJX1mDWgkrGNbZRnc6Rdl6Tr4gIxr2syr9jL0RyNkAx3c4vnGOhINcYzXpfdC2uCjf534M3argnZeU0O7R4Uh7s5QORz6PeQyObRNSSyeXQNicjOTMnBrayhoYHXX3+dyy+/nLPPPhuAk046CWstK1as+zChrq6OK664gq9+9auFMWeddRa33HILU6ZMwXEcfv7zn1NRUcFDDz1EPB4H4IwzzuCSSy7hV7/6FUcccQThcHijx02fPp3q6mruvfdeiouLARg/fjwXX3xxj5KDd999N8YYfvvb39K/f38AjjrqqMJrWuv3v/89/fr144477iAczn+CcOqpp3LuuecWqhtd1+Xoo4/mj3/8I3V1dVRX5z9cefHFF0mn04WE6cb64x//iDGGu+66i7KyMgC+8pWvkMvleOaZZ2hubqasrIxjjz2W//mf/6GmpqbwGubMmUNbWxvHHnssAA8//DDLly/n9ttvZ/z48UD+/68zzjiD5ubmTX7ftpaKigShkBrAyM6hqqpkW09BZIema0i2tmH7VPLh4zUAmCDAbvCQl+MHBI7Bcw3FyRwbfj7TToySjuRgfUkJuWikUDVojcELh4mm0wz+pIFwEODFXFrLYrSW5f8Wro+FqS8pIdqexumomnMDS4YwA9pW4dqAQW0r6dPWQH14RLev4YXRE8jkErS4FbRGo8w8YCwHLL2XUBDgkiRKPQY4dMnb7LNqHpef9D1qSvtQH3JZGY0wOJMl6gd4JqAy51Hndq6eXFWRYN+6VobXZmgKu7zep4ymaJglJQmOXl6Ha/PpvQDwu/kEK+h41ypSWVKReOedfkelYNghE3GJZzsnF8eNilBdvXH/Dhw6IoC3g07bdqmA4QP074j0nH4PiWweXUMim0fXkMiOyyq532NKDm5lxcXFJBIJHnvsMQYOHMjEiROJx+P8+Mc/7jJu/Qq5WCzGKaecwq9//WvmzZvHoEGDePvttznjjDPIZDKFJUkBJk2axM0338yHH37I8OHDN2rc0KFDmTdvHuecc04hMQgwbtw4xowZQ1vbek93b4QgCHj11Vc55JBDCkk1gOHDh3PwwQfz0ksvFbbdfPPNtLa2FhKDAE1NTRQVFZFMJgvbpk6dyqOPPspzzz3HGWecAcAzzzzDoEGDGDt27CbN76qrruLiiy8uJAYB2traiEbzy1elUinKysqYOnUq99xzT6fqwWeeeYbKykoOOuggAJ5//nlGjx5dSAwCFBUVceqppzJ9+vRNmtfW1NiY1JNP0usZk/8jvr6+lW5WMBaRf0HXkHxRKvdJMHRCBUtfaQTA+H6+7yDgej6u7+P4PrX9ioh6Hols5+q2rOsS+AYHSyybLRxbYAy+G8JEDW4qwMOlvTjaZYwXDtFeUkS6KEYo55FoTbI8NoSSxmaaikrZI7Wavi3NFKdStMXXS7AZy8nvzKF/WyM5VtFKGQ8feQhPjT2Qk+e+RpiWTgnNkmyKL8/7Bz+feAorQyGGrLcz7nlku3mAKxtyeWqXfpz2wTKWlcUZ0pokbqOkYmHGNLUQ9X1SoVD+vTAGy7qOiRZojuQrIMfUttEedmlORMBa8Dq+QgZCDkNGRWj9MAsdb3FFqeFbk6CurvVf/x8JHFAMZ44O88iC/K1kSdjy04Oy1NWlNup4kfXp95DI5tE1JLJ5dA3JF2VjH8QT+SIpObiVRSIRrr76am644QauvPJKIpEI+++/P5MnT+a4444rJKcGDx7cKVkGMHToUABWrVpV6Nv36KOP8uijj3Z7rpqaGkKh0EaNW3uuwYMHd9k/fPhwPvjgg016nc3NzSSTyc+Mt35y0HVdVqxYwV133cWiRYtYvnw5a9as6XLcXnvtxeDBgwvJwba2Nl555ZVCBeamMMbQ3NzMfffdx4IFC1i+fHmn9zUI8k8/Dxs2jD322KOQHEyn08yePZtp06YV3ttly5Z1Sgyu/zq3N/rDRnYW1urnXWRz6BqSrc04hsOvGk3Dp0lSjTniFSHevW8pjf9sI2jzMQSUNbdTkUqBYwkMOOv9TDaWJxhwwW7UPLCIJjee/4Hd8CkoGxDKBUSzPplYmC4N+4B0UYz28vyDcX44RDYWYf/XFlDRkiQAWmJF1BRV0X9xE219UrTHozRUlNNaVMQDX/oyQ+trOOnt2ZR6Tey+fCV/GPcl3hs8gmueu5OyTOel9Fsdj/dLExSnsgwq7LO4QFkyC5VFXeYXuC5P7j6IUStbiXk+X/50Na4NiHUkBv2OZUVda7FAS8glZxzeLo4xMutjrCXsW0avaGJ5PEJr3CWNS9hYvrqHw1ljDftWW/wTosyaH1AWgwNHOBhjNunfgFsPyXHpnh7L2wzj+wUUh/VviGwe/R4S2Ty6hkQ2j64hEdkZKTn4BZg6dSoTJkzghRde4OWXX+b111/n1Vdf5bHHHuP+++8H6JIYhHUJK8dxCv992mmnMWnSpG7PM2rUKFauXLlR49Ym49avLNzwvJvCdHw4k06nu+yzG/x2/d3vfsctt9zCsGHD2G+//Zg8eTJjx47l0Ucf5emnn+409phjjuG+++6jrq6OV155hVwuxzHHHLPJ85s5cybXXHMN1dXVHHjggUycOJHdd9+dV199lfvuu6/LOW+++WZWrVrFhx9+SDKZLCwpCuB5HpFIZMNTFBK9IiIiItK9yhEJ6Fi1c/L1u3faV/fcKpq+/hwl2RSLKypxPIMbWNpKo+z3+CRKRhZTdsV+uM+vYeVNiwk26K1njUN9dZzE0gyJ9izFrWlaKhKF/a7v01hZ2vkYx6G5rJjiliyZqEu242EwfCiuyRLpH7CqT192WbCCRDrLqr7l/GPMXhw57y0ueuEZfjVlGh/3G8Tfd53A6XNndor94N6HkE5E2K09SdhawGLIV/sNa2pnXr9SsuGuFYTpSJjVZTH6N6fxjKEs62EBb4NqSQPkXJd3SoqoD7m8fE2IJat9XlztMKDE8OUx6/5G71xjCK5jOHqPzVt+ftdyy67l+hRNRERERERkR6Tk4FaWTCb55JNPGDlyJCeeeCInnngiuVyOW2+9lYcffphXX30VgJUrV2KtXe8GHpYuXQrkKwjX9gAMhUJdqtYWLVrEypUricViDBw4cKPHGWMK51jf+r0QN1ZZWRlFRUUsW7asy77ly5cX/juTyXD33Xczbtw4pk+fXqjGA7jzzju7HDt16lR++9vfMnv2bP7xj38wZswYRo0atcnzmz59OkOGDOF3v/tdoQ8jwN/+9rcuY4855hhuueUWXnzxRd59910GDx7MXnvtVdg/aNCgbt+37raJiIiIyMapPnIA5R+dwdJffUT5wlYSY0pIHDmAykP6dPobua3Ow7GWDR9n88MhkokIqwaWMnRJE5FUhrgD6ZIYiVyWqvZWavtVdzmv6wdYTLdLfS6u7sOkVz6kKJV/oG7EsjV8OrwasPTJNXD13x7jjcTuvDT+QEJ7WQ5a/DaN8QS/mDiNWSPzy+B/WlrEhLp8X2oDNLkOL1SWkfUC6CY5CNBSFKF/U4qMY7orgCxojIZZHguzW1uaSDjMmMEhxnRdyENERERERESkE+dfD5HNsXDhQs4//3yefPLJwrZwOMyuu+4K5KsCAerr65k5c93TxqlUiv/7v/9j6NChjB49murqavbYYw+eeuopamtrC+M8z+P666/nyiuvxPO8jR5XXl7Ofvvtx9NPP019fX1h3Ny5c5k/f/4mv05jDEcccQSvvPIKCxcuLGxfuXIl//jHPwrfZzIZ0uk0Q4cO7ZQY/Pjjj3n77bcLc11rxIgR7Lrrrrzwwgu88cYbPaoahHxPwwEDBnRKDNbU1DBr1iwAfN8vbK+urmbcuHHMmjWLOXPmMHXq1E6xJk2axPz583n//fcL27LZbKf/j0VERERk04VKw4y8bh92ffBQhvx4H6oO7dspMQjQ8GkS+zkps7biCG1FEfxIiLDnU9KYpDUW5+MhQwlns53GxpJZqle3AGDWW+3C2IADUu8w7tOPC4nBtYYuq2dtFWCZn2RQrg5MM4/tfTSTzv8pe1/yM363z5cK43OuQ3MIahMR3utXxt936U9LUQTaPcj4dCee8eibyVLp+WRcFwNE/M5jPWNYURxjQkuSVRE98ykiIiIiIjsf65gt8rUz0l3kVjZ27Fj2228/7rjjDmpqahgzZgyrV6/m0UcfZfjw4YwfP54HHniAUCjEddddx/z58+nTpw9//vOfWb16Nb/+9a8Lsb73ve9xySWX8LWvfY3TTjuNsrIy/v73v/PBBx9w2WWXFaoLN3bcd77zHc4//3zOPfdcTjvtNFKpFL///e8L+zfVxRdfzMsvv8yFF17IV7/6VVzX5dFHHyWRSJDt+CCmtLSUsWPH8uc//5mioiKGDRvGwoULefLJJwuJ0mQySWnpuiWfpk6dyi233IIxpsfJwYkTJzJz5kxuvPFG9thjD1asWMETTzxRWAa1vb290/hjjz2W6667rnD+9Z199tnMmDGDSy+9lDPPPJOKigr++te/smTJEoAuH2CJiIiIyJYzYK9ilrxU36XvYDibwwCOb2mszD8QZgBroO/qeghCuDmf5spiUkVxilszDFxaj+vnk4LRrEc6GgZjGJZbxgB/Dbm6OPUM6HR+1w9Y/6+9XdJL+MnMT8k5hlsOncz1hx+Fv94SoMmiCL/fd1Snubp+QOnqdhqbAigJQ2K9JesDS//6JH3T+b+fM6EQqVAIzxhyjoMx+SVGW8JhBrdlcPyAJnfzlggVERERERGRnYuxGzaEky2uubmZe+65h9mzZ1NbW0tJSQlf+tKXuPjii6murubCCy9k1apVfPe73+XXv/41dXV17L777lxyySXsv//+nWLNnz+fu+++m3feeQfP8xg2bBhnnXUW06ZN69G4Dz/8kOnTp/PBBx9QWlrKv/3bvzFv3jzeffddnnrqqU1+rUuWLOGWW27hrbfeIhKJcNJJJwFw33338eabbwL5ir2bb76ZN998k1wuR//+/TnmmGMYMWIEP/jBD/jZz37GkUceWYi5Zs0apk2bxtixY7n33ns3eU4ALS0t3HLLLbz88su0tbXRr18/DjvsMI444gjOO+88/t//+3987WtfK4xva2srzOnBBx/sEm/lypXcfPPNvP766wBMnjyZUaNG8etf/5r77ruv0zKk20ptbeu2noLIVmcMVFeXUFfXqubhIj2ga0h2RIFv+f0Jr2ENWNfFBJaQ5xHK5jDWkmjPEvICLBAYwBji6SyOv15Kz1qi6YCi9tzaDThYAseQC7nsnZtLP1tLmhjz2Ru73oIrJbQwkJr1Z7RestDy/048k3vGHZD/NuTkv4DiTI5BzSkAVpQlGFjXTq1vaQy5EHWhKAI5H3IBlET46gfLKM/lwFoyrkttPE5ygz6LbhAQAO8WxfjwGvW/lh2Pfg+JbB5dQyKbR9eQfFH69CnZ1lPotR6vfniLxDm57qwtEmdHouTgdmBtcrAnybidQV1dHV/+8pf5wQ9+wKmnnrqtp0NTUxMlJSW4Gzyh/eCDD/LrX/+aJ554gsGDt32zFyUHZWegP+RFNo+uIdlRPf9fH7H0jSSu72OsxQQB1kK/5U0EofyyMGsTg64fsOeqVayOl9CcSBAKfOqrS2mpLCaazLLrByuJep07GI5gEYPJ99JuppwaBtPuJPho+CAOXfUOfVMtZB2XSJBjwzUjHtl/HOed9dVO28pTWQ5a1oDbcZ35BlaHQrQ7Dgsi4c4BSiJQFOb8NxdS7HmFtGTacVhQXk6wdskba4kG+Xl/EI/wxo8Tm/muinzx9HtIZPPoGhLZPLqG5Iui5ODWo+Rgz6nnoGz3/vSnPxEOh5kyZcq2ngoAN998M0cffXRhSVLI9yx89tlnqaioYODAgdtwdiIiIiK9X/XYSoKQSy4SJhcJk4nH8GJRyhqyVK7JUNKQJZb0SbTmSLRmifo+Ve0pQqeMoE9LG0FHj75MIkIuFu4Sv4kq2txiAMpool9sMc8fvgfvjR3Jk/sdQpYwrw0fSdDNcvLVqWbGLqvptG1UfXshMQjgWij3A1xjwF0vRtjJVxA6hnBgO92sxYKA4c3NhIOAcBAQCwIc8kuntmhZexEREREREdkE6jkon8n3fRobGzdqbHFxMbFYbIuef/r06SxcuJB//OMfnHbaaZ36EEK+onBjJBIJEokt9yT1sccey4wZM7j44os59thjMcYwa9YsPvjgA6655ppC70QRERER2TpK+kYhCMBxsB2JMaejF6ATBJQkPcraLYExtMdDLC8pJ57JUTysiHQkxP7vf0ptdSmZSJjiXAYLhQpAh4A4OZr8Adw3YV+KAo9F1UPxHZeQtdSWV2Mdy4GfLmLOqF04ZMHHhSTe3EFD2K3mQ+7/bcBNxxzCwgH9GdrYCm6EtmjnZT8da0kboG8RZPz8BCJuR2/CgJDtXM0I+QRhKLBgwO+Yc7ajF6GIiIiIiIjIxlJyUD7T6tWrOeGEEzZq7I9//GOOP/74LXr+ZDLJG2+8weGHH85ll13WZf/UqVM3Ks4FF1zARRddtMXmdfDBB3PLLbdw//33c/fdd+N5HqNHj+bnP/85kydP3mLnEREREZHuDdq3FMdYAmvzyTRrsQT4xhL3/UKiz7WWopRHa3GIymw7o746kKfvraRkwSr61zYD4GFoDMXw4i79WtuI4eER4p1dR1Jf0peVGyT1fNfll8eczvkvzWB47WrSsXaaY5VUpuo5eMUrzNltbxpay9irLkk8mgJC4AVYx6N9vZ6BSdehIRzKzz/W+bYs4kBgHLB+p+2BMVhgZTRMtiMpGgDqFCEiIiIiIjujQIuo9Jh6DspnymQyvPvuuxs1dtSoUVRXV2/dCW3gtdde26hxgwYN2i56AH7R1HNQdgbqDyCyeXQNyY6sbmGS53+2gPbaLK4LVZ+uoU9NmqKc32VsazzMkFQj/eedRZsHs//9XYo/rsWEHWxbhlw4RktFjPL2FAMaW8iFHV7fd3d8x6EtEe8UKwCWl5Vw8KcLOemNlyn99wPI/fElYsk27KkH8pMTz6LtnkXkop1X1fCBFUUxwNDquiyNR1jpODCgGDboXPjfB2VYdutiXCDU0VcwMIa2UJg3KssI3M6Vgq3G8MZ/btlVPES+CPo9JLJ5dA2JbB5dQ/JFUc/Bref/+myZnoOn1O58PQdVOSifKRqNMn78+G09jc+0Pc9NRERERLau6lEJTvvN3oXvZ+3xF6LdLMVpASew5HBwqmKUAsf94eDC/pf3+D+8IP9pUFNRnKaiOCPbF+N4PoQgks2RjXT0JbQ2/2Sq47Cwb39un3gYP/vlUdR87zDSbv7WKvKepT4Rp9Tv/AmTC7xWVUZ7yCUwhqgf0Lc1ye1HZzhjZpT8oqiWw/v7nLd7wC9j0JQLkXM64hhDfSxM4HR9NLYk6Pq6RURERERERD6LkoMiIiIiIrLDi6dzRD2ftliEeNorbPccB9cGBLHub30CG8bNWhw/IHAMrhewLDyY6hW1ZOJRhjhZhtetJhMOscJUkQ5FqT8gQYADXtdqvQP7Wh4qKuKo5lasMfjGEBiDsZZBqSzzyooASIdcTDTC4QMtq85Js7jV0CduKe7IQ5571QCu/lUjdUVxQoGlORoi3u4TYNiww6AWgxEREREREZFNoeSgiIiIiIjs8Dpa8BHJebQmIkQzHoEx+A5UZlOU3/oZq054kC4O0X9pG839w2TdfHbOALutXsa+dUsLQ/dkCXPjI3GCgIV7DCJqsl3C5VqyrCyN87K1HJBM4zvrUnljm9pYXBQjFXIBSIXdwtxHlHZO8FX1C3PDFeX89o9tvOnHGTUkQuMbrSyKhqm0trAQaQCsCuu2TkRERERERDae7iJFRERERGSHl4qHKG/NEvEDIsk0vjFAQNzzSJswFWeO7va4UCYgFvFoq4yQyGUKyUGAMU01ncZG8Nkn9SmDljaQiKd4fvyILvGKGxrJVQ7FS2fx051r/AxQlcmyPNTRxzCwbNhvcH19+ke46t8rC9+fsDyKaYLVjkPCWizQbgw589kxREREREREeivbTdsF2TgbrkgjIiIiIiKyw/GsYfmAEjJhl8BAMh5idd8i6iJFtIYin32gYyhq9oi2+Tgb9O4LB36X4QZLpd/OEfM/YEAq3WX/nGgfCDl4JdFujoWU64K1kPNxc13jf57vHB6ij+eRM4Zmx6HFcfCNIdZNr0URERERERGRz6LkoIiIiIiI7PBCOZ9kIsyKAcUsHVzGmj5FtBZFwefzivNoLw2TKXIpac1RXBcQy61bKnRVorzTWAsEdCwFGhgGu6u7xGv0Q2AtNSUx/A3Oa6wlm81B2oOcT7m/acnB6j4uA3IeA7I5jLU41jI4myNjdFsnIiIiIiIiG0/LioqIiIiIyA4vWxQhmvULecBQYHF9ixcOGPbLAz/zOGMgF3VprYBY0mGv5Svw4vlkW59kG02xIuJelrDn4RPCdjxfabE4+6a6xDu4n+X2jwyZkMucoVXsv7KJopyPbyCLobUov6SoCSypbioTP8+gCsMniRg5A80RN5+sdB3atKyoiIiIiIiIbAIlB0VEREREZIfnO11vboyB7NhyBp4+9DOPC+UAY/GiLm1Rl5rWCkwM3EhAe3mIlpIwo2sXU94QKlQNgmVpSTVuldcl3pQhFheLj2FlaZyVJTESWZ+Ji2t5rk9FYZx1DNlIuMvxnyedtiyKhmG9vhrNDhjPp2FNjsq+mxZPREREREREdk5KDoqIiIiIyI7PdcC3nTZZgIbc5x6WTsTou7yJTCJEyGbxnBCZIIyfNRzW+gZ9VtYBEOCQppx2U8xCuysfDBiCS22XeMbAjC+n+dofDHVFUcrSOfataWZJIo7doMLPc8zaWW6UTBZC1uJtsE6q4xhiCVUPioiIiIiIyMZRclBERERERHZ4TpBPsvkGAsdgrMUE4GSDzz2udmQV/ZY2kWjzsMUOxvfJhMK4gaXO6Usf8slBh4AEDbw9YDeS9QnqBpfRr5vkIMB+feDwhWuIAYGBwBjqih2MtVTmPLLG0BoOEfUDPrch4gb8wCdkYcN6RcdCotjt9hgREREREZHeyuoZyR5TclBERERERHZ4Ti7AsZY+TWkiOZ9ULERtZRyvPPK5xw04qi+LatoZNb+GhrJiRq+qIRmOEDgO/4ztSsSkGJZeQmAcXhq5P28NPAhvlxCR6OdX/FmT7yvoYnCtZde2JIMyOeIdScyaSIjlkRAQ2+jXuGBpwOCMzxLHIdextGjIWvrkPHRrJyIiIiIiIhtLd5AiIiIiIrLDcwLLwNp23I7kW0l7jnAuIHPA4M897ktn9+XJZWneqUhQ2tTOCioZurKO1SXF/GHivvxy8GRcPCyGdCjCEYuXEsJh8B4hPq8mMUy+JrCQQjROITEI0D+bryDcFMVlhgprSSQz1IRdIhb6eT6trrNJcURERERERGTnpuSgiIiIiIjs8IrasoXE4FqxrE+2Kfu5x0XjLqf/ZATJFg/HNfzx7Hf5dHA/WsIuH44Y2DFq3W1TUyxOn3SaSd8awqwX3/nMuKOGuqz8NEdgLZ5jCPkZznnzb+y98mNWlvbl0f2mkiqrBoo2+jUuanWpd3wWhUP55oZADqgOPn/pVBEREREREZH16RFTERERERHZ4bl0XebTArY+vVHHJ0pDxIpcTr5jTxLGw49GCft+53MEAWWpNF+/eSSR+OffSl10UTk44ALWGL77/P9ywocvMLxxFROXvMdPnp7OyR++tJGvLi8cWBavlxgEWB1yWbOJFYgiIiIiIiKyc1NyUEREREREdniZ0jDZcOfbm2Q8TLrB26Q4xf1jnPTnCRw7uJF9V9cSz+WPj+dyjF+2krOuHkJp1ef3MQSIxRyu+69qDjg4RiLXzrgV8zrtL820c/Y7f9ukuUWLDUE3icDasBaEERERERGRnY81Zot87Yx0FykiIiIiIjs8G3ZpKI8TT3uEvIBsxCUTcrqtKNwYI39xKP/+l0W8/vAHrHKK6LtrCUfcMoJ4WXijY8TiLqedWcYbtR6+MYRs57msLi6neBPmlIgajA0637xaS2gnvZkVERERERGRnlFyUEREREREdnjlgxKsXpyEaAgbMxhrSUccxpwyuMcxK6aN5JhpIzd7bvvuXcrv9jyEcz94ubAtwPDzI7/K9ZsQZ0CJYZAfUOM6eMbgWEt/LyDrgG7tREREREREZGPpDlJERERERHZ4u0w/kDXHvUh7cRisBWNItOUYec3e23pqnDoxxHHTvsbb/YZx5vzXqYsX86uJJ3P6KYM2KU59i6XKD6jyA9IGIjbf07Bpq8xaREREREREeislB0VEREREZIcXG1LEPj/Zi39eM5ecZ4mXhdn7b0ds62kBEAkbZl4R5+czJ3PB+5Mocy3fPCzESfu5mxSnMm5Zu4BofL0VSqPBlpuriIiIiIiI9H5KDoqIiIiISK/Q58Qh9DlxyLaexmf6wdEhfnB0z4/fd4iL61p8f12PQYvla+PUc1BEREREREQ2nrOtJyAiIiIiIiIb5/5vRylLWBxrCdmAo/YwXHhcbFtPS0RERERE5AsXmC3ztTNS5aCIiIiIiMgOol+Zyx9/kMDa/Lqixuykd7IiIiIiIiLSY0oOioiIiIiI7GCUFBQREREREZGe0rKiIiIiIiIiIiIiIiIiIjsJJQdFREREREREREREREREdhJaVlRERERERERERERERER2KNZRu4WeUuWgiIiIiIiIiIiIiIiIyHYsk8lgrd0isVQ5KCIiIiIiIiIiIiIiIrKdWbRoEbfeeitz5syhra2NP/7xjzz22GOMHDmSs88+u8dxVTkoIiIiIiIiIiIiIiIish2ZN28ep556Kh9++CHHH398oWrQdV1uvPFGHn/88R7HVuWgiIiIiIiIiIiIiIiIyHbkZz/7GWPHjuXee+8F4KGHHgLgmmuuIZPJ8MADD3DyySf3KLYqB0VERERERERERERERES2I++++y7f+MY3CIVCGGM67fvyl7/M4sWLexxblYMiIiIiIiIiIiIiIiKyQ7HmX4/ZkUWjUdLpdLf7mpqaiEQiPY6tykERERERERERERERERGR7cghhxzCrbfeSk1NTWGbMYb29nbuvfdeJk6c2OPYqhwUERERERERERERERER2Y58//vf54wzzmDq1KnstttuGGP46U9/yqeffoq1lptuuqnHsVU5KCIiIiIiIiIiIiIiIrIdGTBgAE8++SRf//rXsdYydOhQkskk06ZN409/+hNDhgzpcWxVDoqIiIiIiIiIiIiIiIhsZyoqKvjOd76zxeMqOSgiIiIiIiIiIiIiIiI7FGvMtp7CVvXGG2/8yzEHHnhgj2IrOSgiIiIiIiIiIiIiIiKyHTn77LMxxmCtLWwzGyRE582b16PYSg6KiIiIiIiIiIiIiIiIbEceeOCBLtuSySRvvvkmTz75JLfddluPYys5KCIiIiIiIiIiIiIiIrIdOeigg7rdPmnSJBKJBHfeeSd33313j2I7mzMxERERERERgYa6HDMfquONvzd1WvJFREREREREZEsbN24cr7/+eo+PV+WgiIiIiIjIZvjHjCbmPNmC57oAPPt/jXzrxkGUVUe28cxERERERESkN5o1axZFRUU9Pl7JQRERERERkR4KAptPDIZC0NEY3reGO3+0kqvuHL5tJyciIiIiItKLBWZbz2DrOuecc7psC4KAmpoaVqxYwQUXXNDj2EoOioiIiIiI9FAmHeC7Ltasuys1xuD19rtUERERERER2aq6a1nhOA677LILF110EaecckqPYys5KCIiIiIi0lPW4jkOvuPkKwetJRQEGOUGRUREREREZDP87ne/22qxlRwUERERERHpoZoV6XWJQQBj8BwHE2zbeYmIiIiIiMiOZ+XKlZs0fuDAgT06j5KDIiIiIiIiPbR6lUeXMkFj8ByVDoqIiIiIiMimmTx5MmYTlqKZN29ej86j5KCIiIiIiPRqLRnLKQ/D/GaHEtdy8zGWY8ZsmeRdoiSEBdaPZmGTbuZERERERERk09leeN914403fiH3k0oOioiIiIhIr3bAXYZmHHAMGQtnz7C8/FWPXfo4mx07XZfuNjnoev5mxxYREREREZGdy1e+8pUv5DxKDoqIiIiISK+1rMnSjAthd93yn9Zy4u895l2++fF9H8KeR+AYIr5PgMGxFr8XPsEqIiIiIiIiX6y5c+fy2muvkc1msdYCYK0lmUzy1ltv8Yc//KFHcZUcFBERERGRXmtZow9uOP9NxgMsREI0O2Fg86v79hhfSuKOZRRls4XqwcAYWqPRzY4tIiIiIiIiO6+HHnqIG264oZAUXJ/jOBx66KE9jr356+iIiIiIiIhsRwILf1ro8L/zHcqKXLABNKWgPQPtWWhK4nW9t+qRbNoS87xOy4o61hL1vC1zAhEREREREdkpPfjggxx22GG89tprfPOb3+T000/n3Xff5ZZbbiEajXLCCSf0OLYqB0VEREREpNdY0QYHPBwhCNZucSCdhfWftLRAOseWeFayvCqM6eYpTmMtmbTP4z/+lIalGSLlYaZeOZTBw2ObfU4RERERERHp/ZYvX85VV11FWVkZY8eO5fbbbycWi3HMMcewaNEiHnjgAaZNm9aj2KocFBERERGRXmPSnyIEGIg6+a+Qmy8ljIWhJApFEQg5rJc93Cz1q7Okw12fucyEQvz2nHm8nSlmcf9qPomV8YerF9PatPlLmYqIiIiIiAhYs2W+tlfhcJhYLP+A6bBhw1iyZAm5XA6AAw44gMWLF/c4tpKDIiIiIiLSazRngIgDxuS/Qg6UxyDi5r93HYiHIez2KP7y+W38z/kf87szP6J5RjVNf19MeyRCJpRPEFqgPRLB5HyWDKzOn7NDTd9yHrlh6RZ4lSIiIiIiItLb7b777jz//PMAjBgxgiAIeO+99wCoqanZrNhaVlRERERERHoPt5vHPsMOZNerFDQmnyxk06oH33y+mT8/1kYkFIfqOFkq6TN9AeERQ2mLRmmJRcFaXAwhL4M1G8zFGNY0bpmKRREREREREendzj33XC677DJaWlq48cYbOfLII/nBD37AlClTeOqppzjggAN6HFuVgyIiIiIi0ots5JowFjy/a6/Az/PkY60EBuoScdYUJciEQry4yyiinkdxJkN5Kk1ZOkM8m8ULuZ37HEL+e2c7XrNGREREREREthtHHXUUd911F6NGjQLg+uuvZ/jw4TzyyCOMHDmS//zP/+xxbFUOioiIiIhI72Ftfm3P9av2XAdCBjxbGBPxAhatCNhl6MYvL+obQ2M8Ts4xWCBnDHusqKOyqZ324hipsigu4Pg+nmOJej6ZUMdyptYS8QOs0fOZIiIiIiIi8q/5vs+kSZOYNGkSABUVFdx7771bJLbuTEVEREREpPcI6Ha1UBNyqExnqUplMb6lOpOlqnTTqvhaImGyrpNfLtQYPMehobIYx4FdPlnBwMV12CDI1y5ai2stcc8nlvOIez4ha7frZvciIiIiIiKy/Tj00EO54YYbeP/997d4bFUOioiIiIhI72HJrywaWPDzVYThIGD/lU0U+fmsYcYY0o6hqjzyuaFWrcjx1j/aGLZbjL32jpNxO1cZBo6hpqyEomEBNYMrybouXihENJvFt5ZIziMbCa9b6LRjm4iIiIiIiGy+YMM+773MtGnT+Nvf/sZDDz3EsGHDOOmkkzj++OMZNGjQZsdWclBERERERHoP07F0aG7dpmF1bYXEIEDUWjwLyRwkwt2H+dGN9XzcHsYzIYreSrPH7xtxgwjBBquQZkMuHmAdB8dasJZMJIKbzRLN5XCAnJvfF815WCAy+gpKWurxnRBrrjiDyA+mbMl3QERERERERHqBH/7wh1x99dW8+uqr/PWvf+W+++7j1ltvZf/99+fEE09k6tSplJSU9Ci2lhUVEREREZHewzH5ZUU7EnVYS7HndxkWGENjuvsQz7ycZF57mCIvoCqbwzGG90kQDbquVxr2fIwxBK5L4Lq41mKsxXTUC0Y9j+JMlkQ2h2stey7/iLKWehwgHHj0/eWj2J88uQXfABEREREREektjDFMmDCBG264gZdffpk77riDAQMGcN111/GlL32px3GVHBQRERERkR2eH8C9Hzr55KBvO+1rinYtD2wJOUTcLpsB+P2MNNWZHCW+RzwIKPU8ioKAEBD1/HzSETDWUpVOw3pL2RjAsRZrwXcc1p+JBb48/1kyJGikH4tCY/g0Mool9ywnc94jm/cGiIiIiIiISK/leR4vv/wyM2bM4KWXXgJgwoQJPY6nZUVFRERERGSHN/p3EdqzBpx8n8H1LS1LUJr1qM7m+/01hF3WhEOEN3hUMggs3308h+9BGEuwrlsgsSAgAIp8n7jvExiDay1Rv2s1oQF81yGEJej4HvLT+jSxF5XpfCWj9WBNtISGcDH1szLs7/mY0GdkLEVERERERGSnYq0tLCk6c+ZMmpub2Xvvvfn2t7/Nl7/8ZSoqKnocW8lBERERERHZof33G05HYpB8Js7tXD0YOIZPiuN84ge4gaXM92mKRSiPrRvz4YqAUx+B1ngCd2icLy+pZf00nbVgPR9cB8cYnCAgHAT4jiHWmmXwkjpCXsCqwRU0VRQROIbiljb2/nQJpcl2VlZUsqh/Pyob8onBZChMbbykUF2YJcYzE2dy9JwpOCEt8CIiIiIiIvKvWPOvx+zIvvSlL1FfX8/AgQP56le/yoknnsjw4cO3SGwlB0VEREREZId22wdhcMkvKWpM/r9DFrJBoYowSISoas2SjbisjETBD0hmLYc+7LC8GXBDUJJPBw5tSdKlfs8Ywu0pmsqLqfQ8Yn6+rjCWzDLhhflEO6oShy1cw1sHjaSxOsEpL79CxPN4f8QwWosT7LZsJb7jkDOGBRV9CMy6s7hZn0hblncvf5P9bz9oK79jIiIiIiIisr2bPHkyJ5xwAuPGjdvisZUcFBERERGRHZrnWwg5nXr/5ZOEBjwLxpB1XVZVJfIVhdl838DhdxrwAggsOB3Lg4ZdinN+t+eZO6gvu7W2k1hvKVGDJdKRGMx/D7vOX0nDsDiJbJY/HXIwS/r3y+8cBsv7VTNx7judEoMAxg3o25xk+J+eI/P4bMLDizEPnIIZXb1F3iMRERERERHZsVx//fVbLbaSgyIiIiIiskMLG0uuux1ORxUh6681YyGVg7Cb/3ItZLx8gjCZg1LDvJI4g1tTlK+XJGx3DKOTKSpync+UTkSp61tKnzUt6+aT84l7HrVlpesSgx0WD+jH3gvDXfoihvBxPJcVdjhltDJwQS25iXfhk8NUxbGZBCbsYK48BOeb+238m9OUwqxqwY6pBvUzFBEREREREZQcFBERERGRHVwucMCxnSsHgZAXUJz2aI6Fsa7J5whTHkRDEA+vGxhxoS0D0XzyzEZcnh9YwW4N7ZTlPEJBQHHOJ2It3bW0yIU7J93q+5WwdEAVAxoau51vqW2myVZhzbregv1Sa8jYSiwOTZQRoZVdmIsF0vUJmhiEJYq56ln8Fc24P5r0L9+XyPH34Ly2BDAYAjKnHkAw/eR80lRERERERER2Wup0L1vM8ccfz4UXXrhVYre3t9PY2P2HKyIiIiKyk+uoDkxkPIy1YC1uLsDD0BZxsSGzrh9hcQRiGzwj6ZDfHg2B64BjyDkO7xfHebmihLdL4kQ9j6KWJN4GCUisZXW/UtqLIuTCDkEJVNkmQr7Py2P3IJ7OdD5V4DOyYQmHNr1I/8xKqrJ17N36Lm6y85yaqMbi4FNEjkrCuITwgQB72+uf+VbYGR/jHHUn0aHX4ry2BI8wGeIkKSH82NuYB97u8dssIiIiIiIivYMqB2W7N2/ePK644gp+8pOfbJXGmyIiIiKy40p55JN+1tKvJc2nFQkwBt/kt3vGgYyfX8Yz5MDaROEGy3p2qaYLO5S2eeza2MLkVXVEg4BkKMTyaBHF2RxxzyPs+ZSkUmQrojSPLGLS++9jckAr7LFsOb8/4nBSsWinsGe89SRVXj0A/ZprscAixpIiQQWrGcwSLDEsLmkqyZEgSxGQL3x0OyZu65LQkoER5fDeKpwrnyY0fwnhVApDvioyTRxwcDrSjFkS8Jd58I0Dttz/ASIiIiIiItuI3fDhTdloSg7Kdm/BggXU1tZu62mIiIiIyHYosIC1ONbyaWXRuh0dCUOywbptuQBwOloQdtxEOuQThRsmC4FjVzWwa31D4XvHWvq2tFJTXMzwuoZOY/dYurTTkqPhIGDfhYuYuf9+heVO4+kk45d2rtwzQB/WMIClJMj3LbQYspR3JAZjXeblYPH3uB0PB0tABc2AwSNMlhJCZLE4bLhQjMFCzHb3UkVERERERGQ79eKLLzJnzhzWrFnDFVdcwbx589hzzz0ZNGhQj2MqOSgiIiIiItu95raAnzyS4ZNVAYkwnDUpzIkHR7j0nhSEIgRhZ131n7X5ZJ/XTRrM60gWGgNRZ10V4YZjLZRms/lDjGFJRQXt0UjHvoC04+B2JP1Cvk8o8LucKp7NEvJ9MqEQBpj45scEuLhsONYvJAYhn8SL0IIL+ITIkeg02hBgMEQ6JpohQv7WLj8fr6PH4IYMlvAz86Hvj8gRgdIo3jPnYUZWdX2fREREREREZJtKpVJceumlzJkzh+LiYtrb2zn//PN5+OGH+eijj3jwwQcZM2ZMj2Kr56BsFdZaHnvsMc455xwOO+wwJk6cyCmnnML999+Ptes+eGlpaeHaa6/luOOOY8KECZx44olMnz6dTCbfm+Xuu+/muuuuA+Diiy/m+OOP3+S5zJ8/n+9///tMmTKF8ePHc/TRR/PDH/6Q1atXdxpXW1vLj370I4466igOP/xwfvSjH/HCCy8wbtw43nzzzcK4TCbDHXfcwQknnMDBBx/MiSeeyF133UUul+vJWyUiIiIiG+G829O8vQLaAoc1GYc7ns5x+1MpPq0jv1QogG/zSb6AfBKwu+Sgtflx1haSiYNa04xf2Ugs5+e3B/n9GTd/u7S6pHhdYhDAOKyqLCcZj5GMx2hNxJk7fHiXU30yeDBR36e6pZUhK2sZtrqOWoYW9vs4vFR1CItL+3U51uADljAZWC/RZ4EMMVy8jgpAgyXM2sSgwSeEh0NA13JIQwhLCJ8YKUxLmtCEOz/zPRcREREREZFt56abbuLDDz/k/vvv59VXXy3kVn72s5/Rr18/brnllh7HVuWgbBV33nkn9957L9OmTePkk0+mvb2dGTNmMH36dIqKijjttNMAuOqqq/j4448566yzqK6uZu7cudx///00Nzfzwx/+kMmTJ1NXV8fjjz/Oueeey5577rlJ81iwYAHnnXceQ4cO5Rvf+AaxWIz33nuPGTNmsGzZMh544AEA2tvbueCCC6irq+Oss86ivLycJ598kjlz5nSK5/s+3/nOd3jvvfc4+eSTGT58OPPmzePee+/l448/5qabbsJsR+scb0dTEdkq1v6M62ddpGd0DcmO4t1FHg3pzj+oOcfhz697OKFQPsnnrbfzs6oGIf8D76zbV5rOcdDyRt6rLiEdcjvG5Me93beSilSaZDjSJUwqFAbyD7RZx2HuiOGEgoDdly7Ddx3mjhjBsr592HP+UnZbsBwDZJwIr/Y/kH1WFlFKM3XRKuojffAct0t8i7t2Ivi4+IQBi48LOPnKPxzcDSoEnY7v8/0JPQJcLAbbEcsWokIID89G4C/zMMfv3v37JbIV6feQyObRNSSyeXQNicj27umnn+aKK67g4IMPxvfXrUDTt29fLrnkEq6//voex1ZyULY4z/N49NFHmTJlCtdee21h+0knncSUKVOYM2cOp512Gg0NDbz++utcfvnlnH322YUx1lpWrFgBwJgxY9h77715/PHHGT9+POPGjdukufzxj3/EGMNdd91FWVkZAF/5ylfI5XI888wzNDc3U1ZWxsMPP8zy5cu5/fbbGT9+fGEuZ5xxBs3NzYV4M2bM4PXXX+e2225jwoQJhe177rknN954Iy+++CKTJk3qydu2xVVUJAiFun7QJNIbVVWVbOspiOzQdA3J9i6yKg10XaUhaiFnLa4X4G+4KMranoPdMQbc/P6BrWkMsDoR7TLsg+oSwl5/RrSnuuwLDCRDIRKe1xHS8O7oUbw7cgQOYKylrKWdXRcup640QTIaIeJ5VDS0kovHMKksfTJJDl/1Ee9XDOHj4tHs0rYAQ77noN+xlGiAQ4CLv8GtW4CLgwWCjmMoHLuWg8XBI8DgEcXB79QXcW3KsKgpQ6Ja/w7ItqPfQyKbR9eQyObRNSSy47K9PLvf0tLymX0Fy8rKSCaTPY6t5KBscaFQiGeeeQbP8zptb2pqoqioiFQq/+FKcXExiUSCxx57jIEDBzJx4kTi8Tg//vGPt9hcrrrqKi6++OJCYhCgra2NaDT/4U8qlaKsrIznn3+e0aNHFxKDAEVFRZx66qlMnz69sG3WrFlUVFSw++6709TUVNh+yCGH4LouL7/88naTHGxsTOrJJ+n1jMn/EV9f3/qZn/+KyGfTNSQ7it36WRIhS9JbL/FlLZEYhHyD4wf47gbJQceAH3z2o+A5H/yATMf+4pxPe6Tz7VEkF/B+ZTEDU9mOer0807EkaUNRHC+doTSTzS/iaQyB6+KsTUxaWFlRSltH4jEVDZOMhdm95tNO59m1eSUzB4/jg7I9Gdm4gl2SK3Dx8YjQTmmhGnBDBr/TPgv4uDj4HYnDtdtCQECEdKfj81WFlvYzxpKsa+3+fRLZivR7SGTz6BoS2Ty6huSLUq0H8aSHxowZw1NPPcWhhx7aZd+sWbN63G8QlByUrSQcDvPyyy/z4osvsmTJEpYtW0ZLSwsAQZD/ACMSiXD11Vdzww03cOWVVxKJRNh///2ZPHkyxx13XCGBtzmMMTQ3N3PfffexYMECli9fzqpVqwpr866dy7JlyzolBtcavkHvmOXLl9PY2MhRRx3V7flqamo2e85bkv6wkZ2Ftfp5F9kcuoZke2eM4Y4Lo1zzUJZVzZawgUljXb4yMcK37s6Qi4TA73xMdTpLSXuGZcUxPMfJf/qzNk9oLVgDjsOyRJTRYZc9GlppiIXJdSQZQ55Pn+YUBsuaaIiKnEckgGIvR//mVoqzWTKhELXFRQRBQDIaJRsJg7WEfZ94zqOhtIi2eOclSXNuiNpYGQNSjYVtkcAnHPhkTAybTZAiAYQ76vryaT6nY4lQyC8X6uPQTikltBHuqKp0aSdDOR5h3I51Vr2OfoQWCJPBdCQTfcL57oQ/PQbCIf0bINuUfg+JbB5dQyKbR9eQiGyvLrnkEi677DKampo44ogjMMbwxhtv8Kc//YlHHnmEX/3qVz2OreSgbHHWWr773e8ye/Zs9t13X/bee2++8pWvsP/++3PxxRd3Gjt16lQmTJjACy+8wMsvv8zrr7/Oq6++ymOPPcb9999PJNK1v8ummDlzJtdccw3V1dUceOCBTJw4kd13351XX32V++67rzDO87xuz7VhgtL3fYYOHcqVV17Z7flKS0s3a74iIiIi0r2hfVwe+H/xLtv/+D2HMX+0EDL5BKGFRM5j7JpWwDIomWVOnzKCfJ4tX0pnoSPPhuc4vDCwkqFtaXapb2NFPIzrWxJpr5BLbAq59M16GAKGNTRSlM0n4yK+TzyXhaylsaQ4P9gYcqEQbmAJm84raayT//TJ4GMxZJ0Q+zXOY0hyNRHrkSNCK1UdyUGLwRDCx3ZkQENkcbDESOHhYMhhCeFiKGUFHjGyJMhS3Oms7ZQRHR0hePRsgqyPGVGZr7AUERERERGR7c5RRx3FL37xC371q1/x4osvAvDTn/6Uqqoqrr32WqZOndrj2EoOyhb3zjvvMHv2bM4///xOyUDP82hubi6skZtMJvnkk08YOXIkJ554IieeeCK5XI5bb72Vhx9+mFdffZXDDjtss+Yyffp0hgwZwu9+9zvi8XUfJv3tb3/rNG7QoEEsXbq0y/Ebbhs4cCDz5s3jwAMPxHHWLV3leR6zZs2iX79+mzVfEREREdk0iYSTTwqGyCcIAd86fFyRoKYomq+6C6DQmC/oeDTcrPtbLuc6LCxL0K8tTTTjE/U6L+MZCizLSmI0xFwOXL6qy76lfSu7zCvnOvghl5J0mpQbJXANgWvACRicXkScJGGSgMELElS3h1lb2hgmS5QkGYpxyOKTf4gtn9/MJwhT35yAOWIkoaOGkf3mUzDzE3wPorgdFYLdsQSDq7BDyj9jv4iIiIiIiGwvFi5cyPHHH8/xxx/PokWLaGpqorS0lJEjR3bKT/TE5h0t0o3m5mYARowY0Wn7E088QTqdxvfzH2gsXLiQ888/nyeffLIwJhwOs+uuuwIUfrjX/q/tQX1/U1MTAwYM6JQYrKmpYdasWQCFuUyaNIn58+fz/vvvF8Zls9lOcwM47LDDaG5u5rHHHuu0/bHHHuPqq6/m9ddf3+Q5ioiIiEjPtWbpSPqt25ZxDKuKYx2Vd4bAMfllRR1DlCCfHPQ36ONnLRWZHDG6/s1ZFAR8UF1KYyz6Gd3/unKs5ZDXPsDmQsTSPol2j7LWFONa36DM1hEh2TE7S5h2TMfSoGuFyGEJiFJHmBQuOcJkCJEhMIbIT48kfMwIjOsQ+t8TCa38Ps6aq7FXTiTbvwzXyWE6rbVqsViC/z5mI1+BiIiIiIiIbEtf/epXeeKJJwAYOXIk+++/P6NHj97sxCCoclC2gr333puioiJuuukmVq1aRWlpKW+++SYzZ84kGo2STCYBGDt2LPvttx933HEHNTU1jBkzhtWrV/Poo48yfPjwQg/AiooKIJ+Aq6+v36RS2YkTJzJz5kxuvPFG9thjD1asWFFIUgK0t7cDcPbZZzNjxgwuvfRSzjzzTCoqKvjrX//KkiVLgHyfG4CTTjqJv/zlL/ziF79g/vz57LnnnixYsIA//elP7Lbbbpxwwglb5k0UERERkY1SsnZleN/mH300Jv/fnyETDjGioYVPixJgfXANoZxPLJklHXHZtSFJbcilKeRiLPTL5XDdfIKxPRLmvX6V7Le6oRCvtDXJkFzAkgF98ucGjLX0X1NPqM0vbMtP0eWV4QexS+OnXeZlyGBZt8x9O0W0UUKaGH1ZRIBLior8053Xdt//GsD77hT47hSstdh97yZYlSosTur+v/GYUVUb87aKiIiIiIhs92wvXxIlHA4X8iNbmpKDssVVVVVxyy23cNttt3HvvfcSDocZNmwYN954Ix988AGPPPII9fX1VFVV8ctf/pJ77rmH2bNn8/jjj1NSUsLkyZO5+OKLCYfDABx00EEcffTRvPTSS7zxxhscccQRXXoBfpb/+I//IJFI8OKLL/LXv/6Vfv36cdxxx3HEEUdw3nnn8eabb7LbbrtRWlrKPffcw80338yjjz4KwOTJk5k6dSq//vWvC/0II5EId955J//zP//Ds88+y9NPP011dTWnnnoqF1xwAbFYbOu8qSIiIiLSrbADHSV46xJxxuSrAz/D4n6lRJszZKIRMBDPWdyyKNHW/HKcg7I5RqUzhfFp18FYizWGJ3cdxrLSIkY2tTKsuZ2RtY0cvKKWZ8bvzbL+1ThBkM9PuuFOicGOidHilnQ7p6BjoU8LJCmhlQoMlio+xeKQpRzHGLyvH4y55KB/+b4YYwi/dzH24zrsPxswR4/ERHX7JyIiIiIisqO4/PLL+fnPf05rayu77bYbiUSiy5iBAwf2KLaxPVmrUaSXaWpqoqSkBNd1O21/8MEH+fWvf80TTzzB4MGDt9Hseqa2tnVbT0FkqzMGqqtLqKtr/bzPgEXkM+gakt5i4H0RPBxwOpJxQQBZSzcrhOb7EoYdQulc52NChv6tafapaSHi+0Q3uCg+KU/wfnUp1hiMtexV18ouTflVKA78aBEjl63mtQN2pT0RxwCu5zPp+bmE1lu+1HMdon3qOe6TWV2mtYpB1DCMXZhLK31xjU940mC8O87EVpVuibdJZLuj30Mim0fXkMjm0TUkX5Q+fbp/QFA23z27PL5F4lzwyclbJM6Wtueee+L7fmFlw+7MmzevR7H16KgIcPPNNzN79mxmzJhRqP7zfZ9nn32WioqKHmffRURERGTrsxbodK9kIEy+D2Hhkx4DbscX4Idd7v1ShktnhUjhQAA1pTH6tmUY3JLqUnk4uinJ4OYkTYko5ZkcCS+f9HOCgEXDB7Jo+EBKUymqm1qoLyvBD7m8t89I9pm7iJAXkImEWDGskm+/1bl3NeRzmC4++zKHBf33pnzudwE26EIoIiIiIiIiO5Mbbrhhq8VWclB2OHV1dRs1LpFIdFtm251jjz2WGTNmcPHFF3PsscdijGHWrFl88MEHXHPNNVukwaeIiIiIbB0T+vm8vHqDv9eMARcKWUOn85OW0azPwFLDkos8AB6bB5e+GmXuoHIWVhcz6dNaYn7Q0a8vv+xnOAjo155h7VoTFsg5DiWpFF96dx5jlq3CARpKivjLoQfQXFHEbi0rcXxL2PoM8RqpLyqnT3tj56kCfamhzvQh8sdvbMm3RkRERERERHZQJ5+89SoalRyUHc7UqVM3atwFF1zARRddtFFjDz74YG655Rbuv/9+7r77bjzPY/To0fz85z9n8uTJmzNdEREREdnK/nisz4D7QvlKwbU5QLvesqIG8Gy+atAY8AIG17WxX991/aJP3R0GFaW58Il8j8Go5+MbUwjoWEtLyCWKwbEWY21+vzEMWlPPrstWFWJVtrZzxFsf4BIQ87zC9r5Nzbyyx4Gc8NEzneZvgRaKyX5yI4myyJZ+e0RERERERHol63z2cpu9wRtvvPEvxxx44IE9iq3koOxwbr/99o0aN2jQoE2KO3HiRCZOnNiTKYmIiIjINuQ68PbpGc74e4R/NgPGAX+9AWtzfK25/P8GsCIRY8O2DROGGt7/Nuz/iyyLihOMaU0CFgu8U1nMHs3tBI5L0JEUBMBaBtV1rgQEGFDXRDzIAJBzXdzApy0eZ01pny5jAwzLjjuKfkoMioiIiIiISIezzz4bYwx2vbYXG/YfVM9B2WmMHz9+W09BRERERLYzg0vgH6dm6Xt3BJzO/QKxrKsk7NiVCrk0pX3KYxtGgtF9DS+0lvBxWYLSnEdjJExg4ID6VrIhQ269JeerUmmaSoq7xGgqSbC4tJq5I4extF9fEukMFW1tjFi9qNO4NHFWHTWBvveetHlvgIiIiIiIiPQqDzzwQJdtyWSSN998kyeffJLbbrutx7GVHBQRERERkd4vF3T+3nTdtNaDX3PZ/5YsayIRUiEXx1r28FM41hL3fMLGJ+wHlKUzlORyLBzcjzFLVzCgoRmATMjlhX12Z1WfKnw336EwGYuSjEVJ4bBw/lhifhsGcM7Zh+JffmUrvnARERERERHZER100EHdbp80aRKJRII777yTu+++u0exlRwUEREREZHewzXreg2utXYJlpgLgYVsPivYJ9F9iIhreO1bcN2MNB+tsZw0xvLNI8Jc+W2PZCyCay04Dq615BwHL+Ty1GEH0a+hiXg6w9J+1fiA5zj4xmA7Vn1xA0tbaRGlq74P7RmIh2G9KkQRERERERGRjTFu3DjuueeeHh+v5KCIiIiIiPQeDvm+gj7rkoSugWi4MMQNB/hJ73PDFMUMP/9K59ulSCZHKh4lMIYAaIrHqG5PEg4sBlhTWb5usO/jOwbrrOsH4Tvgr/2+KNrDFygiIiIiIiI7u1mzZlFUVNTj45UcFBERERGR3sMCrpNPEq6tGDQda4j6ARiDH3aIdSkv/NeSxZ0bFGZDIdYUJRjQnuwy1g+FsBs0iscYsqoUFBERERER2SK63HP1Muecc06XbUEQUFNTw4oVK7jgggt6HFvJQRERERER6UXWuzlce6OY8ddrMGjBCwgHAS0ZKN2EAj6vm8Se7ziYIMBusM83zrrzr8fp3feuIiIiIiIisoVY2/WhVsdx2GWXXbjooos45ZRTehxbyUEREREREek9DPmKwbWJOWvXSwx2sOBbSHmblhwcu2eY9z/yOsUuC/mEcj7ZqJM/NRAYQySXJR0JdU4QWks4m9uMFyciIiIiIiI7i9/97nefu9/3/R7H1po2IiIiIiLSewQWMPks3dqvbmRcJz90E1z0zTJ23zWEsQEmCEjE2/j3L0HfxhY8xyl8BY5DeVuKaM5bt7SptYQ8j6ISPZ8pIiIiIiIi/9qRRx7J/Pnzu903d+5cJk6c2OPYujMVEREREZFeIxG2JL18b0EM4AXgmnyp4HoCAyWbUDW41qUXlAHgeR5/+9sL9D/kZPb63svUVpTSnsj3JOxb30TOgcE1jazqV45vDI61OMBpl/bdzFcoIiIiIiIivdVf/vIXPM8DYMWKFTzzzDPdJghfeeUVcrmer0yj5KCIiIiIiPQa75yRZbffR7Frk4FBAD4QNuDZfNM/12DbfZpTluLw5jUBLB1awkejBnPcC2/SUF6ME1hWDKhiyYA+nHfnMP70H5+yut3FRBwmX9CPoaNim/8iRUREREREBNsLm7q///77/O///i8AxhjuuOOOzxx77rnn9vg8Sg6KiIiIiEivURGD1d/M8NDHhvfrHMZV+nzrWReyFkJOftnRXEAosFTGN/986bRlSb9q/jl0IIHrYKwlns4S9TxiFVG+etdum38SERERERER2Sl897vf5ZxzzsFay1FHHcX06dPZfffdO41xXZfi4mKKi4t7fB4lB0VEREREpNf5t10t7OpT19ZRLQj5ykEDuA7xTI54ePNbsIdClkwsgnXysawxJGMRbHoTGxqKiIiIiIjITi8SiTBo0CAAnnvuOfr27Us4HN7i51FyUEREREREeq2yuOnoP9h5uZls2AU2P4G34P32QmKwwBgyW+HmTURERERERHYegwYNYu7cubz22mtks1mszd/DWmtJJpO89dZb/OEPf+hRbCUHRURERESk1wq7EHUsGbtectBafnT4lqnscxwL1nZJPoY6GsiLiIiIiIiI9MRDDz3EDTfcUEgKrs9xHA499NAex978dXRERERERES2Yx+c6zEwHoC1OAR8e1+PC/fdMrHTSZ9YNksklyORyRLPZgnnclS2tG6ZE4iIiIiIiMhO6cEHH+Swww7jtdde45vf/Cann3467777LrfccgvRaJQTTjihx7FVOSgiIiIiIr1aWRTe/cbWqeSLx10S6QyZWAwAYyESBDiBeg6KiIiIiIhsVRus4NLbLF++nKuuuoqysjLGjh3L7bffTiwW45hjjmHRokU88MADTJs2rUexVTkoIiIiIiLSQ2VVETLRaOeNxpCLqOegiIiIiIiI9Fw4HCbW8SDqsGHDWLJkCblcDoADDjiAxYsX9zi2koMiIiIiIiI9lGr3sd08req5utUSERERERGRntt99915/vnnARgxYgRBEPDee+8BUFNTs1mxtayoiIiIiIhID1UMjJB1XaK+X9gWAKkNqwlFRERERERENsG5557LZZddRktLCzfeeCNHHnkkP/jBD5gyZQpPPfUUBxxwQI9j63FWERERERGRHiqriBA4hnTIJec4ZF2HdDgEvbv1hYiIiIiIiGxlRx11FHfddRejRo0C4Prrr2f48OE88sgjjBw5kv/8z//scWxVDoqIiIiIiPRQOOyw694R5s/NEoTyz166ns+hJ5Rt45mJiIiIiIj0btbp/U9lTpo0iUmTJgFQUVHBvffeu0XiKjkoIiIiIiKyGU69oIp3/5HilWdacF3LtHP6MWRUbFtPS0RERERERHqBF198kTlz5rBmzRquuOIK5s2bx5577smgQYN6HFPJQRERERERkc10wGElHHBYybaehoiIiIiIiPQSqVSKSy+9lDlz5lBcXEx7ezvnn38+Dz/8MB999BEPPvggY8aM6VFs9RwUERERERERERERERER2Y7cdNNNfPjhh9x///28+uqrWGsB+NnPfka/fv245ZZbehxbyUERERERERERERERERGR7cjTTz/NFVdcwcEHH4wx6/or9u3bl0suuYS33nqrx7GVHBQRERERERERERERERHZjrS0tHxmX8GysjKSyWSPYys5KCIiIiIiIiIiIiIiIjsUa8wW+dpejRkzhqeeeqrbfbNmzepxv0GAUI+PFBEREREREREREREREZEt7pJLLuGyyy6jqamJI444AmMMb7zxBn/605945JFH+NWvftXj2EoOioiIiIiIiIiIiIiIiGxHjjrqKH7xi1/wq1/9ihdffBGAn/70p1RVVXHttdcyderUHsdWclBERERERERERERERERkO3P88cdz/PHHs2jRIpqamigtLWXkyJE4zuZ1DVRyUERERERERERERERERGQbe+aZZzj44IMpLS3ttH3kyJFb9Dybl1oUERERERERERERERER+YJZ42yRr+3J5ZdfzuLFizttu+eee6ivr9+i59m+XrWIiIiIiIiIiIiIiIjITsha2+l73/e56aabqKmp2aLnUXJQREREREREREREREREZDu0YcJwS1ByUERERERERERERERERGQnoeSgiIiIiIiIiIiIiIiIyE5CyUERERERERERERERERGR7ZQxZovGC23RaCIiIiIiIiIiIiIiIiJbmXW2bMJse3HppZcSiUQ6bbv44osJh8OdthljePbZZ3t0DiUHRURERERERERERERERLaxk08++Qs5j5KDIiIiIiIiIiIiIiIiItvYf//3f38h51HPQREREREREREREREREZGdhCoHRURERERkpzB7UcB//z6Dm/NJRxz+bUqUbxzobutpiYiIiIiIiHyhlBwUEREREZFer6414Nb/aWN4JkMsCGh3XZ540mNYRTFHjNaCKiIiIiIiIjsaa8y2nsIOS3fBIiIiIiLS611xT4pRbe30zWQpzXkMSGcY2Zbijoeat/XURERERERERL5QSg6KiIiIiEivZ2uzxIKg07ZSzyPR7m+jGYmIiIiIiIhsG0oOioiIiIhIr9fkdH/rU5wLut0uIiIiIiIi0lspOSgiIiIiIr3egrIE/gb9KHKOQ2DtNpqRiIiIiIiIyLYR2tYTEBERERER2doSOY8/D6hij9YkVbkcNfEoLU4IBy0rKiIiIiIiIjsXJQdFRERERKTXy7mGmqI4qwaWwdoKwsASbUxu24mJiIiIiIhIz5h/PUS6p2VFRURERESk16tPxCARXpcYBHAMmVjkM4+x1rJyeZZUWn0JRUREREREpPdQ5aCIiIiIiPR6YxvaeKc4isn5VLekybkOTaUxcLs+amqt5RdXLGFhqIQwBqxl36E+53+77zaYuYiIiIiIiMiWpeSgiIiIiIj0aq+8nmJUcysLWoqYtLKJuLUArIqGqUm4QKLT+FuvXcaH0XJK/I6KQWN4d1mId15rY7/xxV/w7EVERERERES2LC0rKiIiIiIivdqMGW18XFXMlEU1JB3D2kVCB2RyDGvOdBn/VK6UEj8gGXJYURylLewC8ODjqS9w1iIiIiIiIiJbhyoHRURERESkd2vM4IQs/9e3CowhEQQc2paiPAhwumlg3+o6fBqNEPIDHM+yqDSO48CQ+rYvfu4iIiIiIiIiW5gqB0VEREREpFdr9Dzeq6wAk88EJh2HtxNRAExgu4wflswQ9QJcCwaobs+SNg4NEd0+iYiIiIiIbC+sMVvka2ekykEREREREenVPi7u2iewwXVxMhmWVJZ02ZeNhAmwZDAU+x7lOZ+0a8h5/hcxXREREREREZGtSo++ioiIiIhIr1bRTVJvUHuSgz5cwMqSRJd9deEQb5YW8U55MbOrynmlooShbUkGN7Z2Gdv46Cc0njOLtic+3SpzFxEREREREdnSlBwUEREREZFebWxDE7s3thS+L87m+Op7H7OifxUZt/Mt0eqlKZKug+es214XjVATibBbJsfS52oAsFmffS9soe7MJwnPXETuwr+zYOiD2G6WKRURERERERHZnig5KCIiIiIivZrv+Xzjk08579PlnLa8hvOWrcL0qeClXYaQdTv3l/jN9StoiEa6xGgKh3Acl9l3Lweg9msvUOxlWXu0A1Sm2/nnhCe38qsRERERERER2TxKDoqIiIiISK/W5rgkmtsIxyL0cR1yiRgtJUXkQiFYr3Iw0+YR83xGt7R1iTEgncEJAtoy+fHp11azYdt6F4td2kbDN2ZuzZcjIiIiIiIigHXMFvnaGSk5KCIiIiIivVpbLEpjdXmXm5+96lpwcuv6EbauSFJTFOe4pSsZ0pYEwLGWXVvb8V2XNYkYz40cAEDShNhwAVELpIIILTNWErTltt4LEhEREREREdkMoW09ARERERERka2pygSdeggCYC1FmSzjV9RS/2kRVSMS3Dw/xqelJQxsTzOttpFkQzOhwOIC/ywtZl5pMctKYwDEPY8cLmHWJRezhIhaH4eA9N8XkzhlzBf4KkVEREREREQ2jpKDIiIiIiLSq1VkUiwtiRMFXCCWyVLe1k4oCBhSZ3n0apdpd+3Jy++k2dOzNJQUE7aWIj8oxKjMZHmzKM7Q1iS+FyES+LSEY9SWFJMOR4jmcvRpaaPMS+KEPT784XuM6lNC5WH9AWh56BNW/WQubtaj+vwxlF99wLZ5M0RERERERGSnp2VFRURERESkVzOEWJFI8OeqchqtpbKllVCQT/wZY4j4Pg880sigjMWh602SCQKq2pOUJ9P4fo6TbkvjATWlpSQyOQY0NZPI5lhVVkorRTRTwpC2euq//gyZNWmWf+sfzP3JfFaF4ywvKuHj/13K0t0f/qLfBhERERERERFAyUEREREREenlchiGtiVpDYUY2Nza9SbIGFrfbibo6EOfcd3CrnDOw/UDIsAhyRSTVjXyoRvHCzn0b2mluj1JSSZLn7Z2+rW2kQ272JzDx2WDCIzDqv98g2XP1OKF18VMRSIsswmWXPHyVn/tIiIiIiIiIhvSsqIiIiIiItKrLStNsNeyNeybTNEcjVKZy2HW22+tpSqdI9WW5J9VZYxotawoihLN5Ri/qJbS9iQALcUJaqqrOHhlLa61RD2PNqJkTAiXgKJshsAxOBiMtdQkyhkwZyW5SKLLnHIhl8V/a8AUv8bQ68d/Qe+EiIiIiIhI72GN+deDpFuqHBQRERERkV5tXr/SfFWg6zJ/YF+WlJd22t+aiGOMoT4W5q2qUv48qh8zh/cj43uUtScxgAHK2pJUNrewS30rbaEoPoYi0pTYFFlCNJhinMCSDTsEroM1hjbPhWBd78KYn8UNfGJJj0TSY/UjS6n51Ttf6PvxecKPv0LJ6b8g9NzcbT0VERERERER2UpUOSgiIiIiIr3aOCfNqopy6Hiq9KO+1awsKaYilabS88mFQ0RSaepjUSo8j8ZoFIDd1jR2iVWUSlPV0ExZkKGM9LrtNsMyU0XWcSkJtZNoS9EQLiLaliQTS+DaHHu1LKXYT+NjqKOMOlNBJnBZ+cuFlOxfRdERQ3v0+szqJgi52KqSHh2/VtWQ83EyOQBiL3yAV5ag8Z93blZMERERERER2f4oOSgiIiIiIr3a/jW1vB0q7rStKR6jKRaluKkFC3zYp5KENfjuusVVaovjjKlr6hzMWppiMcpy6U6bI/gkyGKCgAGp/DEDaaSeIhrDZezTtJBimz/GxdKPJrAh2tJxkm6EBefNYZ9Fm5gcrG2mes9vr5ua61C/+G6IRjYtDhCe/ldMR2JwLbc5iTtnHv7E3Tc5noiIiIiIiGy/tKyoiIiIiIj0auGGJNWNLWBtp+1lOQ8DNIdDpMJhAEozXmH/M7sOozm2LtGWcxz+sucI3hrej+46WxiCTt87WGJODrCU2DQpotRSSS2VJIkRJUMYn7AfYDKb3iujas9vF5Y8NYDjB1SOvHiT4wCUXP+HLq/JAKX/dnOP4omIiIiIiMj2S8nBzXT88cdz4YUXbutpyHbgqaeeYty4cbz55pvbeioiIiIisp7VJSVUNrYSyeVIWcuikEudHzCotp7yhmaire2FsYNbUpSl8xV0DUVx7jtoLJ/078uifn144oA9eG3UQJ7abzR1saJO5/AwBNYh7PidtjvGx00HtFBEHVWkiVFPKfPNUBaZASSJYLCErcem6i6d6OT8brZuhvbMlo0nIiIiIiKyhVhjtsjXzkjLioqIiIiISK+2MO0Q7VtJyA94qyQKxrAyFGL54AH8aPZb/O3QfYiTT7aFrGWvNS2kXMNudY0MbWmjtD1JPJlmaG09u9T05beH7MmZl53Oo79+lBIvQxaXduJ4YZfasgRFLUkqskkAjPUZmKqlgTJcIEmYVidemFuriRMNslR5TcT2upHMj76M+4tZOMkk/ncOxz/3EHA385nOdJai/3cv7uomWm/+JvFfP0Xi9y8BYF1D2/87vttEo4iIiIiIiPROSg6KiIiIiEiv5gaWbCLGW8UJWO+p0LZohCd3H8HbQ/vSpz3D8KYkET+gJRpiTF0Te9Y2AGAw+cRhELDPshoum5XmzsP347ZjD+as2R+SDocwFoqCFEPS9bREYlRk2yiihb5BGxaYxx6AS9qEu07QQF9bj7c6i3PZDMAjRCvhqx/HXP04AQEWB5cA8HD7hHHbs5/5eqv6fh2biJE5ci8iL83DbW4rJP8iB32/UyLQ+JbSX/3589+/Fz8i+tg/CL+9GFtRRvKbh2HHDMTGI5j2FDguQVUJxMPYimJCs+ZSds4tmKyHjYZoueZ0wp+uJn3+UQSjB27E/2MbKZUFP4Di2JaLKSIiIiIishNQclBERERERHot3wsoNy6pkEvWGLCWY5eu5LBVq3Gt5aOKMl4NAmqLotQWRfN9CY1h6j8XF2JE05lOCbVd1jSx/5IaWuJRVlYUUZ7MMax9NaPbatadF6dwjAHaTZSE9bqt0IuTpJIluORIUk2CWpyO/oUBhhzleEQJMIRox9SmcLCf0fewY7nRZJrEU290u39ThIDK024G3I6ja4m+voB8hwoDWMBgsRgC/IoQTmP7utee8Sj70e8xQOLe58iNHUrzrJ9s4iw2EAQU/efDxB94HnI+mWnjaP31+VAU3by4IiIiIiIiOwklBzfBM888w/3338+SJUsYNGgQl156aaf91lr+7//+jz//+c8sXrwYz/MYMGAAxx9/PF//+tcxxjB9+nTuv/9+/vCHPzBy5MjCsUEQcNxxx7H33nvzs5/9jJaWFm666SbeeOMNGhoa6Nu3L0cffTQXXHAB0eim3fSOGzfuM/dNmzaNa6+9FoDVq1dz++23M2fOHJLJJMOHD+fss8/m2GOPLYy/9tpref/99znzzDO54447APiv//ovJk6cyKpVq7jjjjt45ZVXSCaTDBs2jNNPP52TTz55k+a71mOPPcZjjz3GsmXLiMVi7LffflxyySWMGjUKyPf4u+666/jf//1ffvvb3/L6669TXFzMlClT+Na3vkUstu4J4kwmw29/+1v+9re/sWbNGvr168exxx7LeeedRzgc3uRxDQ0N3HbbbcyePZtcLsfkyZPZZZddevQ6RURERGTrefb2ZeTiMSpSGfZobSeUyTIqCPhk2BBKUinGrq7jyH8uY+auw/IHGIPrBzjWFmI4QdAl7qDmdp44cDdm7L8L33/iH0x6dW6n/S4BXsftVn24lJSNkPEjRKxHhlChgtFYS6VNUcsY+jKfImqxhMl1LHQaYPEoLsT1KCGMj09AiBCw6b0KN41L59tGt+Oc66c+6aitdHEbU10irD8y/MFSzMJV2FEDejyj2EMvkfjNM+u+f/J1goGVtF93Vo9jioiIiIiI7EyUHNxIaxNRe++9N//+7//OsmXL+I//+A+MMQwYkL+xvfPOO7n33nuZNm0aJ598Mu3t7cyYMYPp06dTVFTEaaedxtSpU7n//vuZOXMmF110USH+22+/TW1tLVOnTgXgqquu4uOPP+ass86iurqauXPncv/999Pc3MwPf/jDTZr79ddf32Xbo48+yocffsiXvvQlAGpra/nGN76BtZYzzzyTkpISXnzxRX70ox9RW1vLOeecUzi2pqaG3/72t1x44YXU1tay1157sWLFCr7xjW+QzWY5/fTTqaqq4vnnn+e//uu/WLp0KZdffvkmzfnpp5/mpz/9KccddxxnnHEGjY2NPPzww1x00UU88cQTFBev+4DkyiuvpE+fPlx22WV88skn/P73v2fRokVMnz4dAN/3+c53vsN7773HySefzPDhw5k3bx733nsvH3/8MTfddBPGmI0el8lkuPDCC1m5ciVnnnkm1dXV/OUvf+G5557bpNf4RdhJe6nKTmTtz7h+1kV6RteQ7AwWz89gTALXWg6rb2JlaQlrKsoBaI/HaIvHGL94VT45aICQwY+EeGdgNZM+XQWA77qEfL9T3IV9yjv2OTw+YVe+/+ozbCjUkbj7pGg44aRH2o/i4VJh2whwAEuFbSNBBotLikqKaMCnpBDDJ9Ilrk8CQ5J1ibqtqbt/ID6vB6ILdE2mrh+t+L//j7bfXtbjGUVmze267fn3SV6v5OCORr+HRDaPriGRzaNrSER2ZkoObgTf97ntttvYY489+M1vfkMolH/bdtttN6677joAPM/j0UcfZcqUKYVKPICTTjqJKVOmMGfOHE477TRGjx7N6NGjee655zolB//+979TXFzMIYccQkNDA6+//jqXX345Z599diGOtZYVK1Zs8vy//OUvd/p+5syZfPjhh5x99tkceeSRANx+++1ks1keffRRqqurATj99NO55ppruOuuu5g2bRqVlZVAvrruxz/+MVOmTCnEvPHGG2lubuaBBx5gt912Kxz/3e9+lwcffJBp06YVKv42xtNPP83IkSML7y/ALrvswq233sqCBQvYd999C9srKyv5zW9+U6jsq66u5t577+WVV15hwoQJzJgxg9dff53bbruNCRMmFI7bc889ufHGG3nxxReZNGnSRo974oknWLx4Mb/85S+ZNGkSACeffDJf//rXWbRo0Ua/xq2toiJBKORu62mIfCGqqkr+9SAR+Uy6hqQ322WfYmre8wiAXDhELtK5519bPE4kgMlL63hhZB+Cjk+Hnt11CIFr2GdlPVW+T1F7CkM+7TV7zGDeH1RdiFFXXESAwWFdtaEForQD0OokGEAbg2wjUVKkKWbdUINf6CcIAZ375+UX7GSDbT4+IcIkN+/N6bGAz04QfnZicK3YWYcQq96Mf3d2Hwx/favTptCYAVRvTkzZpvR7SGTz6BoS2Ty6hkR2XFbZ/R5TcnAjzJ8/n4aGBi688MJCYhDySbebb74ZgFAoxDPPPIPndX5yt6mpiaKiIlKpdcvrTJ06lenTp7NgwQJGjx6N53nMmjWLI444gkgkQnFxMYlEgscee4yBAwcyceJE4vE4P/7xjzf7tXzyySdcd911jBs3jssuyz+tGwQBL7zwAuPGjSMUCtHU1FQYP3nyZP7+97/z2muvdVpedP/99y/8t+/7/OMf/+Dggw8uJAYBHMfhm9/8JrNnz+all17apORgv379eO211/jNb37DtGnTGDhwIIceeiiHHnpol7Ff+9rXOi35+W//9m/ce++9vPTSS0yYMIFZs2ZRUVHB7rvv3um1HXLIIbiuy8svv8ykSZM2etycOXOoqqoqJAYB4vE4J554YuHnYXvQ2JjUk0/S6xmT/yO+vr4Vu+EnpyLyL+kakp3B4RcM4J1zPyEVi+J8xs95S1GcbNglcNb98RQ4hmd3GULOhMg5ljPfmMdHgwaQjoTIFJWy36pm3u9Xiuc6HP3hP8kSwcOQIUIxScAhQzUV1NMnmaQ0m6GCOpqp7HJ+HxcHnxgNWIo67XPxCAr9/iCffPNxsWxMIm7zeeRvG9cmAz/vvBbwP2Nfx4iwS/1R+0Fda49n5JwzibKHZ+OuaAAgKInTfPk0/M2IKduGfg+JbB5dQyKbR9eQfFH0EJtsj5Qc3AirVuWXExo8eHCn7a7rMmTIkML34XCYl19+mRdffJElS5awbNkyWlpagHwCbq2pU6dy++2389xzzzF69Ghee+01mpubC0uKRiIRrr76am644QauvPJKIpEI+++/P5MnT+a4447b5J6DazU1NfG9732P0tJSbrzxRlzXLWxva2vjhRde4IUXXuj22Jqamk7fV1RUdIq7tsfghkaMGAGsew831vnnn8/cuXP5zW9+w29+8xtGjhzJYYcdxkknndTl/4f1ezcClJWVUVZWxsqVKwFYvnw5jY2NHHXUUZ/72jZ23KpVqxg0aFCX/cOHD9+k1/hF0B82srOwVj/vIptD15D0Zo5riMShwXVJYYjkcmTXe7CsOJliVXUl8WzX5TmdIOD4V+bSnoiTDIdpKC8FY0h4PmMa2on4Pm4yyfV/mcEy05cmk1/63ljLYFtLUyJGNBlQnM0AECdJK2X4dK5ezBAiSgOQKFQJrktTWsIksR0VhB5hICBEBovtZtFPgyVMO2EiBITJAQGmh4nEALBlYZzm9o7YPjZaAhVF+NUlEHWxpUXYsjje2IHkDt+D6K1/If7Um+tegTEQcclO3I3WB79Dl1LITeT3r6Rx9o1E/vImJpUlc9w4bN+yzY4r245+D4lsHl1DIptH15CI7IyUHNwE6XS6yzbb8ZvDWst3v/tdZs+ezb777svee+/NV77yFfbff38uvvjiTsf079+fffbZp9B3cObMmVRVVTFu3LjCmKlTpzJhwgReeOEFXn75ZV5//XVeffVVHnvsMe6//34ika69Rz6P53lceeWV1NbW8pvf/KawRCisS1weeeSRfOUrX+n2+A2TYWsTi+u/B91ZG3v9yr6N0a9fPx5++GHefPNNXnzxRV555RXuv/9+HnroIaZPn84BBxxQGLt+Nedavu/jOE7hv4cOHcqVV17Z7blKS0s3aRx8/s+CiIiIiGxfQiFLwguIBJZFiRh9cjl8YwgFAVEDsWwW3xicICBw1i2XeeT7n1KezFCezFBTXd6lIc0uq5s5ZeZrfFg0Ese3mCxEshZrDKuoJBykaaEIxwa0E2FOeD9caxjkNRaSegafLC59qcVgSVeWEm5oxKMIMORG9CX327MIvbuU7OkHQST/t28rUN336928Wktun2GkZl7D2rVLElc/ROJ/ZgLg7TkEs6Iet6kdQ0cdoANO0H13wQBo/Of0TXq/vd/+O+2AaU1hQy7EN+3eZWPY4jiZM7+0xeOKiIiIiIjsDJQc3AhrK9WWLVvWabu1lpUrVzJy5EjeeecdZs+ezfnnn98pGeh5Hs3NzV2Sa1OnTuWnP/0pixcvZvbs2Rx77LGFhFsymeSTTz5h5MiRnHjiiZx44onkcjluvfVWHn74YV599VUOO+ywTXoNv/rVr3jrrbe46qqr2GuvvTrtKy8vJxaL4Xke48eP77SvpqaG+fPnE4/HPzN2RUUF8XicJUuWdNm3dlu/fv02ab4LFiwA4KCDDuKggw4C4N133+Xiiy/mkUce6ZQcXL58eaeqvcbGRtra2hg6dCgAAwcOZN68eRx44IGFhCFQWM517dw2dtygQYN455138DyvU2Jy+fLlm/QaRUREROSLkcjmaLERXAsV2RwZYE6fCuoiIfqnsxy/aBlOMsXIhnKGN6cItbaxx7LV7LGirhAjms52iTv+vX9S0pojmssvpek7hqQTxgQGz4Qo8zxyRGk0CVZWVBCE839jNmdiDGmqYwirKGc1vnF5192LYSsv/MzXkB07+DP3dWbITun8937yxn8jeeO/dR61sIbo394mM+1A7LA+VPb9Olu6W7Ut+ex7CBEREREREdl2PquLvKxn1113ZeDAgfx/9u47vq+67v//45zPzE7TdJdOoAXKRoYoAjJlOkBxoAgIeKFeXHLJpX6dP1yoDEUEFARRoYJsQRAVZAoFtYAUaAt0j7RJmvlZ5/z+SJs2TaHpDG0e99stN5LzOeN1PuQ0OXme1/t922239egYe+CBB7rnpmtubgZWD6O5yp133klnZyelUs+5N4444giSySTXXHMNzc3NHH300d2vzZo1i7POOou77rqre1kqlWLSpEkAPYKrvrjzzju59dZbOeGEE/jQhz7U6/VkMsnBBx/MY489xiuvvNLjtUsvvZQLL7ywxxx8a0skErzzne/kqaeeYsaMGd3L4zjmxhtvJAiCdc4V+FYuuugivv71r/d43yZPnkwqlerRtQjw+9//vkfX3k033QTAYYcdBsAhhxxCc3Mzt912W4/tbrvtNr7yla/w9NNPb9B6hx9+OK2trdx5553d6xSLRe64444NOkdJkiRtHSuikGQuzz+HDea1miruHjmEOeVZ2pNJZleWc8vEHdi5sZkRS1ew07IVHPLqvB7BIEDjoGqShdVDjwZRzPh5y7qDQYBEFJOJV64TxMTFBHEQ0FBZ1R0MAuQzSeZV1JOliRRFsnGOKLF5RqGIien89OHrX2/icDr/633EY4e89YreMUqSJEl6m4qDYLN8DER2DvZBEAT87//+LxdeeCFnnHEGJ554IkuWLOH3v/89NTU1AOyxxx5UVFRw6aWXsnDhQqqrq5k2bRp//vOfyWQytLe399hnbW0tBx54IH/+858ZNWpUj26+KVOmsPfee3PVVVexaNEidtppJxYvXszUqVMZN25cr+6+tzJjxgx+8IMfMHjwYA466CD+9Kc/9Zj/sLy8nEMPPZTPfe5zTJs2jbPPPptTTz2V4cOH89hjj/Hoo4/ygQ98gIkTJ77lcVZtf84553DqqadSX1/Pww8/zDPPPMPHPvaxXvMCrs8nPvEJLr74Ys477zyOOOII4jjmvvvuI5/P9wo4n332Wb7whS/w7ne/m+eff5777ruP4447jr322guAk08+mXvvvZcf/vCHzJgxg912242ZM2dy++23M3nyZE488cQNWu9973sfd9xxBz/84Q957bXXGDNmDPfffz/Lli3boHOUJEnS1hFQYlkmSz6RoDkR0p7qeRu0uKKc5rIsJ//jBRpHDCFMJ8mlkmRWhoEt5WW8PnIYiVKJMQuX0JlOsd/Lb5Au9J6nMBFFBMRkowJ50rQERQqp3j15UTIgQ1c3YkxAtmbDb81avvNRqr76u+7hQGOg+VfnEw+u2uB9FSePJjGj90gYuVMP3uB9SZIkSZLe3gwH++jd7343l19+Oddccw1XXnklQ4cO5Wtf+xq33norAIMHD+aKK67gpz/9Kddffz2pVIqxY8fy3e9+lxdeeIFbbrmFZcuWMXjw4O59HnPMMTz22GMcddRRPY4VBAE/+tGP+MUvfsGjjz7KHXfcQVVVFYcffjjnnnvuBs3f9+qrr1IoFFi2bBlf+cpXer0+YsQIDj30UEaPHs0NN9zA1VdfzR133EFHRwejRo3iggsu4CMf+ch6j7Nq+6uuuorbb7+dzs5Oxo8fz9e+9jVOOumkPte7ysknn0wymWTq1Kn87Gc/I4oidtllF6644ooeczMCfO1rX+OPf/wjl19+OfX19Zx//vmcfvrp3a+n02l+/vOf88tf/pKHHnqI+++/n/r6ej70oQ9x9tlnk81mN2i9RCLBlVdeyZVXXslDDz1Ee3s7Bx98MB/96EfX+R5LkiSpf9UMy/L68q5bn8SbzBOdBGo68xTbOyhlUszYeRylGKIwYHlNFax8onTHOQtJlkpMfmMx7YlMr/1EcUiWfPcQnYlkTLaUpy3Zc4jNwcXlpCkA0BAMYejd6577+63kzj6a/MkHUvn5XxCXZWj9ydlQmd3g/QCsePj/o374GT3mHYyB9p+8+VCnkiRJkqRtUxDHb3J3rC3uwQcf5Ctf+Qq33nprr+FItX733HMP3/rWt7j66qt7BYaCpUtb+rsEaYsLAqivr6KhoQV/mkkbzmtIA8X3vjiTR9P1ZBMJCAKer8jSvEb34L6LlvKBWXNJd+TIV5UTBAHtqRSvDB/adaGsNGrRUnabOYdsscDkN5aSIKaVDMHKSC1FgXLaWcIgMkSkyVMcmWXE4td4rm4XSmFXZFiWz7Nv00uUx+20UcGCUeMY+8/Ttu6bsg7hjHnUHvNtwvYcpepymh79DvGIuv4uS9sxfw5Jm8ZrSNo0XkPaWoYM2fCRPdQ3Pz74b5tlP198/LDNsp9tiZ2D/SSOY26//XamTJliMChJkiRtQS9SSTa5+tZn17ZOFqdTtCVCxjet4PjZ80i2ddJZW0WCrr8MlRcKjGtYzsKaagqJkJFLlrHjGwt4bdQwSARMfmMJEDOIFVSQIyIgTZEAyJMmQUw57SyOysmWShy0/HlmZsdSVihSWcjRHA9iDqPpKE+z2z/XP1LH1hBNHs3y16/t7zIkSZIkSVuY4eBWViwW+epXv8qiRYt48cUXueSSSzZ4H6VSicbGxj6tW1lZ2T0cZn9raGjo03rl5eWUl5dv4WokSZI0ULSWZwnX+DoBjMx3Dem5U2s7M0cMg9ZWdu7o7LFdbUcHj9VU0Z4IOWLWXGJgcX0tI5c3kSOkjg7K6CRJ1GO7CjopEbKIQVTvUUW8KEFFqZV92mYAkCfJv4PJFMIUu0173xY8c0mSJEmSejMc3MqSySRz585l/vz5nH322Rx++OEbvI/Fixdz4okn9mndb3zjG5xwwgkbfIwt4ZhjjunTemeffTbnnHPOFq5GkiRJA0VnIqS8BBGwIJOiMZmgLIrYdUUbbRUVDF3WyOO7jGLsv18nUyp1bxcBL9dUUt2RByAAxi9czBvjRxATEBATrhUMAhRIspAaKoIcQ685jHi3l+hsjygSUiKkkzTt2ZB9Xj6JRNZbMkmSJEnaGHEQrH8lrZN3ov3gd7/73SZtP3jwYH72s5/1ad2JEydu0rE2p77WPGrUqD6td8IJJ7xtgk9JkiS9fTVnUwxd0spz9bUsyaS6lgFNdUkOWdbIxNZWvv6Rcq5eOox9Fy6mslgiF4Y8MHwIy9NppixeRgyUwpAlVVX89/8bzorfFamgg7VvRXMkWU4VQQiDv7EHyYo08T/OpOPQ35BZvoKOZJp5FUOZ+KtDDQYlSZIkSf3Cu9FtUCaT4YADDujvMjbYtlizJEmStn2D8nn2fX0eD4ys77G8MxHyclUFX7x4V/YeG/CdspAnRgzllUyGpZkMlcUiezStYGKxwLKR9SQ7c1S3tFBfnyQm1ysYBJjFCFKJTna9+0hS7xgGQDCsivKXzgOgGpiyhc9XkiRJkqS3Eq5/FUmSJEnadk1evJTWyop1vtaeSrL3zl3dhL/7XCUNtVXs2tlJQExjJs2/B9Vw/YSxPD2kjlxFGZniqmFHe0eDRUKiIKQqzJPYs77X65IkSZIkvR0YDkqSJEnarj07aiilTIr6fJEdVrSyw4pWAFJRREW8es7AZAhzq7JMH1RDPpHosY+/DxlMEATMHl4HQBsZimvdTi0NaiAZM/jwYYTpnttLkiRJkvR24bCikiRJkrZrpTyk45jzpz3PoI5OAOZXVfDojuNoyKaANADlg1LsuLyZeVEI2WyPfeSCgKBUonFEFQCJnctpeiUiTYEiISvCMqIgZERhORU3nb1Vz0+SJEmSBqI4WNdkD+oLOwclSZIkbdfiTMCQZU3dwSDAqJY29p+7kNY17iXDRMChZR28/6XZBHHcYx8HzF9CZWMrx76vBoAhvz2KAgE5MkSkqIyKVJXyVHzroK1yTpIkSZIkbSzDQUmSJEnbteFLW0gWi72Wj2puYYf2zh7LvnTRMHZZupxzn32RMU0t1HV0cvSsOXxwxixyUcRJB5UDkBxVyfPXVlL2zqHEyQBqUgy66t1Unzdlq5yTJEmSJEkby2FFJUmSJG3Xdmtspj2TIrNWQJhLJ9m9sQWo614WBAHzB9ey78IG9l3U0L08CgIe2XEMp685ak0yYNLjH2bRokYSCW+tJEmSJEnbBjsHJUmSJG3X4tosfxk9grlVFd3L5lRV8McdRjGnsrLX+s9NHMO8uprur9vTKZ7YfWcW1lZtlXolSZIkSdqSfLxVkiRJ0natoxTwzMh6/j5iCGMaVwAwp6orFNyprb3X+vl0iof22ZXKjk6CGFrKswzuzDG+pRWo35qlS5IkSZK02RkOSpIkSdquLU6myBaKdCQS3aHgKs3J3rdEpQACoLUs27Ugjqkq5BnV1ASM3eL1SpIkSZLWLw6D9a+kdXJYUUmSJEnbtaW7DOGwWXMg7v1aWdx7YTEVkowiMsUSVfkCwzs6qV7Rxr6HVG+FaiVJkiRJ2rIMByVJkiRt1y55f5rpO47sagdcWzLRa9EXT8lSCANyyQQt6RRtQUBczHPIZ+walCRJkiRt+wwHJUmSJG3X9qiH2eXlDI1jyqMIgCCOGVkskVxHYHjorkmu/WIFkyYEDE+188m9Ovn6LyYRJr19kiRJkiRt+5xzUJIkSdJ2r7oUMbJQZGlVlmQipDZXYHSuyPJM785BgCG1Id87owKo2LqFSpIkSZK0hRkOSpIkSdru7bainWdH1NGZ6goDV1RkaM+kGN3U1s+VSZIkSZI2Rhysa+4I9YXj4kiSJEna7i2sKusOBldpKE/TnF5356AkSZIkSdsrw0FJkiRJ272G9DoGTQkCWta1XJIkSZKk7ZjhoCRJkqTtXkdqHR2CAZTFW78WSZIkSZL6k+GgJEmSpO1eIhVAKui6Awro+m8CanKFfq5MkiRJkqSty3BQkiRJ0nbv/L2jrk+SIaRCSIakijE7DbJ1UJIkSZI0sBgOSpIkSdrufekdMXtV5QkLJYIopqo1x/5LG/nuJ7L9XZokSZIkaSPEQbBZPgaiZH8XIEmSJElbw4MfjOgs5XmjIaIihNFDKvq7JEmSJEmStjrDQUmSJEkDRjYBk4Y5gIokSZIkaeDyrliSJEmSJEmSJEkaIAwHJUmSJEmSJEmSpAHCYUUlSZIkSZIkSZK0TYmDoL9L2GbZOShJkiRJkiRJkiQNEIaDkiRJkiRJkiRJ0gBhOChJkiRJkiRJkiQNEIaDkiRJkiRJkiRJ0gBhOChJkiRJkiRJkiQNEMn+LkCSJEmSJEmSJEnaEHEQ9HcJ2yw7ByVJkiRJkiRJkqQBwnBQkiRJkiRJkiRJGiAMByVJkiRJkiRJkqQBwnBQkiRJkiRJkiRJGiAMByVJkiRJkiRJkqQBItnfBUiSJEmSJEmSJEkbIg6C/i5hm2XnoCRJkiRJkiRJkjRAGA5KkiRJkiRJkiRJA4ThoCRJkiRthKgDHvjm8zz5k9codJb6uxxJkiRJkvrEOQclSZIkaQM98ct5zP/bzgRzX6csn+cPf1rKqFNG8+6zxvR3aZIkSZIkvSXDQUmSJEnaAI2Lcrxy33Le8dwcUvkIgKqlnbT/vBkMByVJkiRpq4iDoL9L2GY5rKgkSZIk9VEcxdzy5VmMf62BVD6isyxJPpsg01Eg7Ih57bev9XeJkiRJkiS9JcNBSZIkSeqj331uBkFniVSuROOwcmo6Oqjq7CRDkVSuRMMji/u7REmSJEmS3pLDikqSJElSH5RKMYtaAtKJFOX5HJVNncQElAhIEFPd1smCqrL+LlOSJEmSpLdkOChJkiRJfdCwqEA6ht3//TrV7Z0rl8aExORJkIoiKon6tUZJkiRJktbHYUUlSZIkqQ9ybSUIYGjDih7LAyAkJpdO0tzeP7VJkiRJktRXdg5KkiRJUh8ky0LqWpsohSHJUs8OwRh47MBJZBOp/ilOkiRJkgaYOOjvCrZddg5KkiRJUh+MGJVm/KuLWTCihniN5REwZ1QN80bVU9fW0V/lSZIkSZLUJ3YOSpIkSVIfhH+eQaIdojJYMLSSmuYcAI21WUhBolBkl8l2DkqSJEmS3t4MByVJkiSpD4plZcwZMYiRS5pors3QUb46CMylU9S2ddA+rrofK5QkSZIkaf0cVlSSJEmS+iA8aAyzxg0j3VkkLK0eWDQGlg6rIxHAon8091+BkiRJkiT1gZ2DkiRJktQH0bxWCCFXlqKso8TywZXEYUDt8lY6y7MMX7CctkRZf5cpSZIkSQNCHAT9XcI2y3BQkiRJkvpgeSbLTq8vppQOeHnKDrSVlZPuLLBwWB2VDa2Men0JL9SN6+8yJUmSJEl6Sw4rKkmSJEl9sOKNdoI4z8Idq1mRLWfii3MpFGJeGTOcf+82jjvftz9za2r49bfe6O9SJUmSJEl6U3YOSpIkSdJ6FBe0868vP88ru0wkChMAvLzLGOYPru5ep5RMUEonmf1ae3+VKUmSJEnSetk5KEmSJEnr8dwXpjFzx5HdwSBAPpuirFDssV4unWJ4Q9NWrk6SJEmSpL4zHJQkSZKk9ZjVnqIY9h54JRFFPb7OFEukiqWtVZYkSZIkSRvMYUUlSZIkaT3eGDKYQjIkFcU9lleu6KAlmwEgUSpR39pOyxpDjUqSJEmStow4CPq7hG2W4aAkSZIkrUecCOlMpQgKBZJRTAwUEgneNec/zGwbz+L6alJRRLpUIkgk1rs/SZIkSZL6i+GgJEmSJK1HTfsKmquq6EinCeKucDBVKjI0v4Q/jj2QfLLr1ipdLDJ2ybL+LVaSJEmSpLdgOChJkiRJ63Dvz+fRcf8MAqA6lSIGAlYPXfPel5/g2R126g4GAfLJJMvKy/ulXkmSJEmS+sJwUJIkSZLW8qsLZtCxNAfpoZR35kiX2oiBVTMOLsukuXb/Y6ksFknGPechbK40HJQkSZIkvX0ZDkqSJEkauOKYzO/+TuqP/6Y0qJbCJ99NaWQt7UtaKWsoMmJ5M5WFVubsOIx0KU8+mSYGOlZ1C64VDMLqzkJJkiRJ0pYTee+10QwHJUmSJA1MLe3U7PW/pFvaiICXh+7Ign8+TH1LAw37nsB+jXNoqq+iOVtJWSHPlHmvMn2HXYgCSBWLFBOJde42EZW27nlIkiRJkrQBwv4uQJIkSZL6Q/rC35BuaaNEwM/efSa37n0SI1fMY1TrAi67/wcU60Ma6wexvLKORdXDyaeq+MC//sSg9k72W7iYd86dz47LllOZy5OMou79JkqGg5IkSZKkty87ByVJkiQNPG2dlN35HDEZEsCp/7yPB3d8Jzfv9UFymQxl+Q6CuOezlK2ZSlKliKrOzu5llfkCQWsbDVWVtCegqrOTqsYVwMitez6SJEmSJPWR4aAkSZKkAadity+TXmO+wGGtyzl09rM0ZIdRSHawvKaaskKh13bP7bBXr2Vl+TwBUNOZgxiK2RQ/Pus5FtUPY9eDBnHKkWVUlm/duTB+8WLI16eliAg4bccilx9c3KrHlyRJkiS9fRkOSpIkSRpQVrzRSl17vtfy0SsWApAqlqjo6CRKhZSCkGXlZeQTCapyOdrSaSryPbctJbo6DNszaTqTCagsp8BgalqX88SjCf76TJGff6WSyvKtM6vDx/+c4M+zINHeQRgE3PxiigfeSPPSR3ufsyRJkiRp4DEclCRJkjQgLHxkHrMveZp/DZrERUGasrhnN93ystruz5PFEssqynijtpZcquu2aXFVJYPbOsgUi91zDMZAU1k5xDHFAMIoIgoCUkHAV/52E+OXzeUPUw7js98+gWqKnP/pKibvnNmguktRTCJ8687D370a8j9/TxCVICyWSDTnKA0qIw4hbO5kOWUbdMxN9fMXQv6/Z1PEMRwzqsTX9i8xoSZe/4aSJEmS1EcxW3eElu2J4aAkSZKk7VOxRNV7vkfw6jJ+v+dRLKweQqF6Ih2ZLDcdcAJnPfV7GrM1NGeqGNq+jIfHHkgUBHSm00TEtGZS3cHgKo1lGbJRJVVt7Yxd2EBjZQV1hRZmjxhMEAQkgDCOKQLt6SzpqMRp0x/inyMn8ezoSXzjxhzEOVJRiXfsEnLeaVWk0yGlKOaWx3LMmhsxojbmhdcDlrTELMkFvFaRJZ8IyJQi9h5S4tKTkoypDVjaGjOvMeaqGQnumhlCJoR0QNSaIxpRBUHXjXI0JAkdeZ5bHLPPsN43z8vbYxIB1JR1vdZehBUdMaffG/NSW4ohcYFTd4zYY2jMLc9FPN8Q8K6dQ759WMCfZ8GkIQF7Dl/dFXn8fUmeXpLsOn4Af5wf8MepIb87psARY/oeEH7+0QS3zE4REvOd/QqcuVu04d8DkiRJkqRegjiOfXxT2g4tXdrS3yVIW1wQQH19FQ0NLfjTTNpwXkPanr18+xzafvQY1Z1tZDs7CRIlKgut3L7XMSyrrOMLD1/PS7U78p/6nYGuDsC28ixzhw8lDkOIY/KJkKXla3XcxTG7L1rM2PnL2G/6bABuO2I/Xh85pMdqyWKe6275f0RByGt1o3hs7O7cvM/R3a+HUUQyiomDgI4woCWRIEVXsBgDrUFAKQyZXVtOPlgdvCWjiHFtnXQmQxqrM7SVpSCKga4grmvnQXcwuGbdLG4lUSwBIaVkAMQMJqA8XwKgNZ2gKZ0kJiAIIK5KQTJBWCgRhQFhS45ka757f4V0ijhcedwwhEKx640clIXMWs+hRjGJUsROuTZamiKCAMZNSJFKhKSJOf+AmF2Ghdw+A5pzcNUrKRoLia790XV6ZWHEQyfm2WlQ374HAF5YFnDv7JD5HQE7VMNJ40pMqu35D97zywIenBMyqgJOmlCibB2P0L7cGHDfayH1ZTHv3zGiMtXz9dnNAfe8FlKTjvnAjhHV6b7XOJD5c0jaNF5D0qbxGtLWMmRIVX+XsN36+rHTNst+vn3/fptlP9sSOwc1IMRxzB/+8AfuvvtuXn/9dYrFIiNGjOCEE07gk5/8JMHKP548/vjjXHvttcyaNYvBgwfzsY99jJdffpmnn36ae+65p3t/s2fP5qqrrmLatGkUCgUmTZrE2WefzUEHHdRfpyhJkqSVnvnhCzw1PckuFXUcNGsGibir4yxJGxf+9Wr+sMexlKJkdzBIHJOIIsrbOgiiqCscDALSUUyyVKKYSHTvu6YzR6YUkc0XAGiqLqeQShHEXUHfKjsvfYMXh+/Ij9/zSVqzFRDHlBVLdCTCru7ERIJ8MiAOAsI4JgVEQFsiQUxXSBgChbWGySmGIXliFlVlKeZjyBe6wsB0V82EdH29LhGUwkRXUJhIUB1FVOSKq16iKZXsHpYnDoLuYC5KJQgKpdXBIEAQkCoUySdX3VKWVr+2ruOHAaUgpH1hiezK/S54Jc+iQVnaE2kevCtiULLE8o6V2w5KQGnlikFXgR2EHHxXlumndDK8Yt2nuKbfvxpy/sNJSIXdYell05P88j15jhvb9T1xyysJvvDo6vP+xX8S3Ht8vkdAeO9rIWc/lKQUd63z8+kRf3p/oTsA/OvcgE88mKIQdb1+5b9j7j85z5CtO5KrJEmSJPWZ4aAGhJ///Odcf/31HH/88bz//e+nra2N++67jyuvvJKKigpOOeUUHn30US688EImTpzIf/3Xf7FkyRIuv/xyysrKKC8v797XzJkzOfPMMxk8eDBnnHEGyWSSBx54gC984QtcfPHFHHXUUf14pj2t/cC4tL1Z9T3u97q0cbyGtL2a8Y92wmwVR/7n6e5gEGJiksSk+eD0+/j3kF26FkcRyUKRAEhQYvKsN3h1/BgK6a7WsMEdOZaXZSmEAZX5AuMamwBYNKSGFRVlLBw+mDSwQ+MKFldVdA9Dmg9CLjnsDDrSKxOiICAVA8WI5mSC3BrdfVEQQBzTFoY9lhUDegSOq7QnkxQLazzeHsVQiCCd6Er54rhrP1HcFfCFrO7AW1kLpYhstHqYzs5E2PNYlSlIru5YjJMhcRgQRGs9Vr/qWGsqRpBK0EsQ0DiojKHLO7q+BOqac7QPzxB3xiwvJOg6AVbXzhq1RzEkQz76UJq/nZxnfb73TLJXF2UpDvj+v1IcPy5HHMN3n032mKfk+WUhd72W4LSdV4ed338m0R0MAsxsDrn55QTn7tG1ziXPJruDQYA5rQE3zUjwxX3WCEy1Tv4ckjaN15C0abyGJA1khoPa7hWLRaZOncpRRx3FN7/5ze7lJ598MkcddRRPPPEEp5xyCj/+8Y8ZNWoU119/PdlsFoA999yTCy+8sEc4eMkllzBo0CB++9vfUlbW9ceeD3/4w5x33nn8+Mc/5rDDDiOVWmucoX4waFA5yeQ6/igjbYcGD3Z4BmlTeA1pe9OZTFOZa6essDpAStNGgiIQEFBkZNsiABKlUo/evFSxxLv/8wzTd5zEsso60lHMqJZWygqdBBGkVgZqbeVlNFevvnYScUx9azv5ZIH3/ecRbt77mNXB4BoS0JWKrf1XqHX8VSoZxaSiiEK4OqQL4pjiujrzSmuEdoUIoggKa3Te5dYK0+KYYhiyKoxLrDmWVkiPYHBVfVE2SaK9sHq1Nxt/6806F4HCWr+fhlEM61p9XbteuWxZPkF9/Vv/uxXFMQvbC+usZUF7SH19FYVSzOL2Yq/Xm+IM9fWJNdbvHUQ2Rmnq67tupxd29H59WXH161o/fw5Jm8ZrSNo0XkPStmtdD1Oqb7xb0XYvmUzy4IMPUiz2vPFvamqioqKCjo4OXn31VebNm8d///d/dweDAIceeijjxo0jl8t1b/Pcc8/x4Q9/mFwu17181bqXXXYZL774InvttddWObe30tjY7pNP2u4FQdcv8cuWOT+AtDG8hrS9qi+1Mq+snuZsOTWd7QQUVwaDABEBMKx9Ke9Y+Cz/rNu91/ZBFNNUXtOVWYUBURAwad5MjpzxDFP3Po72TPk6b0LTpRI/vOe7/HGXd7OirIquNKvnejGQiOKVKWHP5WsLgUktHbxSWUY+EZKMIka258iFYe+V1zxMHHQFhGvuPFyjK2/lsKIrEgFlhYBEHJOJYjKliFwi7Nl9uOb5FaPVg4fGMVkCOrvXWeMM3uKX0KrWXI+vOzNrDB+65j9Eb/ZvUhxz1uQCDQ0db3qMVY7cIckDcxOQ6FnPUaOL3dsfPjrFX+at/p8REPOu+g4aGlYXcNSYJLfP7Pk/7JChnd3rHLVDkhtf6vn6ocNyfapxoPPnkLRpvIakTeM1pK1lfQ+2Sf3BcFADQiqV4rHHHuORRx7hjTfeYO7cuaxYsQKAKIqYM2cOAGPGjOm17bhx43j55ZcBmDdvHgBTp05l6tSp6zzWokWLtsQpbBR/sdFAEcd+v0ubwmtI25v3Xbsvt509nfunHMTx0x+nOt+yxqthd2R34MJpFOOQFwbv1mP758dMIgpXhz1xEDK3bjTDWh/g7Kdu4Tf7nkRDeR2lRM9AKFUs8L33nsW0HXbrzuoiYgICYrryrjjoOngYx13DiQLEMaWA7vXWrLSqGLFHUystiZDkyrqLlFieTlJIrBESpsJVG3UN67m2VeuuCv0SIUVgQXma8jCGIKSQSawejnTVMKWrNi+WqCmWCEoRpQDSMbSGXZ2UhUS4ejuAjkKPbVcdNyyWSLUXVkWUdCZCltSUQa7EPoNKHL1jzNXPBbTkoRjQOyAMYc/BEZ/bvdSnf7N+/O4i0d/hoQVAIiAAjh0T8d39C93bX/7uAhc8Cn+ZFzKsHL6yb5EpdXGP/X/vnUVyRfjTGyGDMvDFfYq8c8Tqdb6xf5GWPNw9O6QqDefvWeLIMZH/rm4Afw5Jm8ZrSNo0XkOSBiLDQW334jjmi1/8Io8++ih77bUXe+yxBx/4wAfYZ599OPfccwG6uwrT6XSv7ddcFq0cRuqUU07h0EMPXefxJk6cuJnPQJIkSRsiM6iMj912AMkPXkFVfjYBCWLSdMVDATFZoJMA2H/xs3QmqplVM4pSmGDJ4FoW1w9eu7GPhopBXHPwqZzz2M2c+Y9fc9PeH2bW0LG0lWUIYijP5ahe0cHTk3fr7pwLgA4CCANSUUwQQFAqMSifI8hkKQQBjWUpyoo50sWAMJGiGAREdGV8AdASBhSSIWEUUwoCOhIh7UHMHslOlrYHrAgTNNWWdYVxa3b7rR0QJgLIBuxXE3PMZGjIFfnXvIB5SyMagyS5TEA6FVPqLJEuRtAZ01aegTAgUSiRaC+wKA5IhQHJKCbsDia7Ys8xVRGn7xlwzwz4d26tWlb+xW3RWQUWNGeI44jqbEBZOiQMil2NjGHXO/bfB3Q1Es5qyvHuOzLdzY4k4I1P5CjbgNH7h5bDb48pUoiKJIASqzPUVYaVw++OLlCIIBmsu+lxUBZ+dVTxTdepTMPVhxe58tCut9nROyRJkiS93RkOarv3z3/+k0cffZSzzjqrOwyErkCwubmZUaNGMWrUKADeeOMNDjzwwB7bz507t/vzkSNHAl1DlR5wwAE91ps9ezYLFizoMSypJEmS+k/xD1+gsVAk9Zfnef3XrzDxb/+gvJSnLVXN7/f6EEe+8nfGNM/nkPn/ZGz7PGYOHcOc6qGUwpBE1DNcS0QRcweN4D8jdmbPBf9mh9Y3eGbH1R2H+WSC3Z99nf+MHUlHNk0UBHSGIZ1hSG0uR3smQ7ZYJAgCxk7OcOaBZeyza4pUMgB6z00IkCvE3PdMgd/+J2BWMs1R42K+957e0+gVowI7XBdTSq28vUuFUIy7PqArZcwmeOWsBLWZtWPPVV+veb4r2xsprJHxhSs/3kzXfj5/ECxpLzLl98k1HsGPmfuJruFER9YE9BpTdY3zCYKuAG5SHSw5M8ecFihPQv2636I+WbOpcn3r9GU/b2btaRolSZIk6e3KcFDbvebmZgDGjx/fY/mdd95JZ2cnpVKJXXfdlWHDhnHXXXfx/ve/v7tb8Pnnn2fGjBkMHz4cgPr6enbddVfuueceTj/9dIYMGQJ0BY3f/va3efXVV/njH/+4Fc9OkiRJbymVpHDM3ow6Zm86+TCdnQXe+PTdHPLMv1lWUccOzUtJEDGhcTETGhfzrtnT+c4xZ7C8ooZEFBHGMWEcE4chuSBgxpCx7L7gJeraOnj3zGd5cvyeFBNJojDkjQlDGd7QyPyRQ4GYbCliSLFEkM/xngMzHLBfJTuP7z1SxZvJpALe/840738nvPkkfF2h1MKziyxYkefx12P+67mKrkStFHcN9ZkMII6pzWz427cxXXBDy2HJpzpZ1tnVSbcxx11ljNOzSJIkSdJmZzio7d4ee+xBRUUFl156KQsXLqS6uppp06bx5z//mUwmQ3t7O2EYcsEFF/DlL3+ZT3/60xx33HE0NjZyyy23kE6nCdb4q8iFF17Ieeedx8c//nFOOeUUampqeOCBB3jhhRc4//zzqa2t7b+TlSRJ0lvLphj7uw+yaNLlDG5ZTkzPcSrLCzkOmflP/rD34aQKxZ43TEHArotnExLwjrn/4R1z/8MeC17lZ4d8pGvbjhzj31jEitpKAgKSUYlCCF+5bAey2bU79ja/kdUhp+wBe+yQ4913Zbpa5RIhxDE3H5Hb4sdf22AH1JAkSZK0BcWO6b/RDAe13Rs8eDBXXHEFP/3pT7n++utJpVKMHTuW7373u7zwwgvccsstLFu2jCOOOAKA6667jp/85CcMHTqUCy64gD/+8Y80NjZ272+PPfbguuuu45prruE3v/kNxWKRsWPH8s1vfpPjjz++v05TkiRJG2CH6g7mtZYDTb1eG9a4nFGLlpLLZugsW51wDVvRwO6LXu2x7h4LXmVk02IWVg9l4uuLKe/IUdbZyRev3YkwhGRi69+sThoUs+RTncxeAUs6Ag4c9uZdh5IkSZKkgcdwUAPCXnvtxXXXXddr+ZFHHskFF1xAqVSisbGRI444ojskXOWWW26hrq6ux7LJkydz2WWXbdGaJUmStOUUnvky8z7zEJPuvLvHXHQxUFhSQfXYdtqjuEc4WFboXOe+atrb2GH2q9Q3ttJclaWmuY10qv+fYJ1QDROqDQYlSZIkST05ZboERFHEsccey3e/+90ey2fOnMns2bPZbbfd+qkySZIkbSm7/+Rw/rLzfuRI0kGGZqqYHu7CvPpBZIpFalesoKqlFeKYII5JtIbk6DmBXlO2iuZsHbmyDKUg4LUx9RSyfZ9XUJIkSZKkrc3OQQlIpVIceeSR3HXXXQRBwC677EJDQwO33nortbW1fPzjH+/vEiVJkrSZpbMhe/z5ozxxahlT/vUvwkLMDuFMqvarZuFiCIKAYcsaGTN/MXu9Mo9Mochr7Eymtokh7Q3MrxnGX8YeRLatkzDI8edDd6eqvY0wtltPkiRJkvT2ZTgorfTVr36VsWPHct9993HvvfdSWVnJ/vvvz3nnnUd9fX1/lydJkqQtIFWWZM97TgFO6V5W29BC4VP/IVUqdS0IA9KFIgDtYQUzKsfyr3eMZ9TcxVQtbaeKdoIopi5qYd6waqqKxX44E0mSJEkaWOKg/6dz2FYZDkorZbNZzjrrLM4666z+LkWSJEn9qb6KIz5Zyx9vbiNVLLKiopyOKvj3uB15bpfxFJIJdn3lDbIdue5N4jBgSEMjj+w5gWEt7f1YvCRJkiRJb81wUJIkSZLWMuKUSZx1Csye3sbieTkWX/IS/9hjp+7XY3o/oRqFARWFIk1l2a1ZqiRJkiRJGyTs7wIkSZIk6e1qwh4VHPS+OhYMGtJjeXN1Ra91oyBk4oKlZBxWVJIkSZL0NmY4KEmSJEnrUdXZc6jQhcMG0zComnjl16VECHHMoJZWRjU3b/0CJUmSJEnqI4cVlSRJkqT1KG9rJl0okE+luhYE0FGZpSUuEcQxUTJBKQyJA8gnU/1brCRJkiRJb8FwUJIkSZLWY/6gYQxuaaWlvIIogDHzllCezxEnwu7uwWSxRK4sTbIUv+W+JEmSJEmbLu49Fbz6yGFFJUmSJGk9ak/eg/YwJFMqUVYsUd/Q0mudTGfXXIMVpfzWLk+SJEmSpD4zHJQkSZKk9Tj6k8NIViYob+sgAhJFKGtbHQKGpYhBy9sJSjFH79VvZUqSJEmStF6Gg5IkSZK0HmEY8Kn/GkZtUys54IG9d2LMrOWMm9nADq8vZ9ILi2itTBMWCoz57OT+LleSJEmSpDdlOChJkiRJfVA3OsOSmipKmQzlmQR3HbUXiwdVUSLgxV1G0FaeZsq54wkrU/1dqiRJkiRJbyrZ3wVIkiRJ0rageVYbLTUVANS0d3DAv2YyfEETAVDVnmfmzkNoaY37t0hJkiRJGiCiIOjvErZZdg5KkiRJUh80v9JCHHbdQo2Zt5QRK4NBgGxngbGvLSPoKPVfgZIkSZIk9YHhoCRJkiT1QTpbJCwWaUinqFrW1uv1ypYcLOvsh8okSZIkSeo7hxWVJEmSpD4oH5yhIZvgU/c9xrBlrXSQ7vF6a2WGondYkiRJkqS3OTsHJUmSJKkPgjBml9cWMHpZM0lKJCl2v5ZPJXh+j3EMHZnqxwolSZIkSVo/n2uVJEmSpD5oySUZ2rQCgAAop0CJIvlEkj+/dy/yqQSBz19KkiRJkt7mDAclSZIkqQ+GTqgkCuIeyxLENAytppQIiYBwUe+5CCVJkiRJm18cBP1dwjbLx1olSZIkqQ9SmQQ7LFnG7GGDKYZdt1KLB9fw1B47s6IsS7azk2xNop+rlCRJkiTprdk5KEmSJEl9kByaJYiSNFZW8PzkcTTXVNBelu1+vaWigrnzVjCmH2uUJEmSJGl97ByUJEmSpD6adP1BJKMiuXSyRzC4SufM5n6oSpIkSZKkvjMclCRJkqQ+GnTwUHbaP8mo+Ush7jn/4NilCyg7emI/VSZJkiRJUt8YDkqSJEnSBhjz9QOpbW1ltxmvUbOilcq2dnae8zrjXlvC7icP7+/yJEmSJGlAiINgs3wMRIaDkiRJkrQBMkPK2P93B1Edt7DLy68z+dU51Dbn2OHO91FV5S2WJEmSJOntLdnfBUiSJEnStqZyQiXRBa2cccYZLFrUSCLhrZUkSZIkadvgY62SJEmSJEmSJEnSAGE4KEmSJEmSJEmSJA0QhoOSJEmSJEmSJEnSAOHEGJIkSZIkSZIkSdqmREHQ3yVss+wclCRJkiRJkiRJkgYIw0FJkiRJkiRJkiRpgDAclCRJkiRJkiRJkgYIw0FJkiRJkiRJkiRpgDAclCRJkiRJkiRJkgaIZH8XIEmSJEmSJEmSJG2IOOjvCrZddg5KkiRJkiRJkiRJA4ThoCRJkiRJkiRJkjRAGA5KkiRJkiRJkiRJA4ThoCRJkiRJkiRJkjRAJPu7AEmSJEmSJEmSJGlDxAT9XcI2y85BSZIkSZIkSZIkaYAwHJQkSZIkSZIkSZIGCMNBSZIkSZIkSZIkaYBwzkFJkiRJA9r0386j4acvkSgUaJw0lJN+sy9B6NwVkiRJkqTtk52DkiRJkgasF6bOo+O7z7JgSDWzxg6HRW3cftRj/V2WJEmSJElbjJ2DkiRJkgaseVfM4PU9JhAlu26NFpRnqVvexLyXWxg9qaqfq5MkSZIkvZkocMSXjWXnoCRJkqQBa3ltZXcwCEAQ0FJZwcPXLeq/oiRJkiRJ2oIMByVJkiQNWMVk71uiUiIBTZ39UI0kSZIkSVue4aAkSZKkAWtwc1OvZeVt7SQS0dYvRpIkSZKkrcBwUJIkSdKAtXPDPJIdOWIgBihFTJr9OpnqRD9XJkmSJEnSlpFc/yqSJEmStH2aWT+axrpaiOOuBUHAjHETqTUblCRJkqS3tTgI+ruEbZadg5IkSZIGrAX1Q7o+CYKuD2DZ4GoqykwHJUmSJEnbJ8NBSZIkSQPWoPblvZaliwXCjrZ+qEaSJEmSpC3PcFCSJEnSgDVx/iIGtTV2f50sFdhz/gvMa6vox6okSZIkSdpynHNQkiRJ0oA1omMRF/71Yf49alc6U2VMWfAfygqd3H3opP4uTZIkSZKkLcJwUJIkSdKAFSUjMqU8+8/51+plQDF2zkFJkiRJ0vbJYUUlSZIkDVjTR+5KKQh6LHt1yESWLvNWSZIkSZLezuIg2CwfA5Gdg5IkSZIGrFeGjOHOycex28zXSBVKtA1O8cCuh1CRHZg3iJIkSZKk7Z/hoCRJkqQBK10oUTk7yeLCeACi5ZBqgUFjM/1cmSRJkiRJW4Zj5UiSJEkasEbOXU42V+z+Ooxh/KylLJhbfIutJEmSJEnadtk5KEmSJGnAKu/I91qWzRUoNeY263Hy+RL/9+UGwlJMBIwcn+bCL9Rt1mNIkiRJktQXdg5KkiRJGrCW1Vf1WrZkWA1xWWKzHuf//ncpYQlyiSTFRJIls/NcdfWyzXoMSZIkSRpIomDzfAxEdg5KkiRJGrA6ypK8vMtIxs1aQrJYYumwambuOIya9tLmPVAY0pRO01CeJohj6tvzFGds3u5ESZIkSZL6wnBQkiRJ0oB1wMsv8MQeezG9bkz3svLOTpaS7v4639BJojxJonzjbp8KhRJLyzI8Nq6etkzXPmo68hw2e8mmFS9JkiRJ0kYwHJQkSZI0YL1RP6LXXAsJYhJxzFM3zOHF+5axY8NcgjimYfhwPnDzARt+kBheGlrVHQwCNJel+c+Q6k0rXpIkSZKkjWA4KEmSJGnAygXrviXK5AtU/eTvfH7eCySIAGiaVcHLX4VJ39mwgDCXj2jKlvVa3pxNA/EG1yxJkiRJ0qZY+yFZSZIkSRowGgbXkk/2DAgLiQQVhU52Wfx6dzAIUNvZRu3tT1LKbdh8hIkQajoLvZbXdOa7Pz/4DymG/irD0F9l2fW3qQ08C0mSJEmS+s5wUJIkSdKAVcgkaaypoq0sSz6ZpK0sy/LaasYvXUi0jqbCRBTz2n6/4eV33ExQ6lvXXyIRMKGhldqO1WFgfWuOMcvaANjrliSvtiQgCCCAhkKC4b9Kv9nuJEmSJElAHASb5WMgclhRSZIkSQNWeWcHLaVKJjS/RrbQyYvDJ5MoRbQXsixLVFNDG0USpMkTAHMqR/DgPgcwrLmRKd/9D5y1el9/e6SNa+7tJB+GJIj53PuzHHJgOb/+Wx5i2HNeEwCpOGZQvkhx5ZCiC9oTXY9trnFTGsUD8wZVkiRJkrTlGQ5KkiRJGrCSQcQX//pzhnQsB+DExJ94YOThNAd1LEyPIEpUsrymiur2FnbqnEHdiiVUN7WxeNAghjfWd+9neUOeq+7LM6ZYIqBEBFxxZ479dk9x7+MRWWKqChGrIr9CEJAqrR6ylF5PqxoOSpIkSZK2DMNBSZIkSQPW7g0vkYxLTN3r/cytHcWo5oXs9fq/uW/nHZmTHUEpnewK7uKYXd4YwSmv3MJfkwfSXszw+pBR3fv5/37cyOh8QCEMu4aliWPqC0Uu+H+NhJXllJdWBoPxyqFIg4Du5sB1DWNjNihJkiRJ2kIMByVJkiQNSM+e+kfGtC3h1/ufxtLKri7ApvJaXqsaw7B5S1k0dtjq4C4IeGncWOYuGs6EhjdYOHE4Na0tdOYiLryilY5cQHpVMLhy/SQBf6urYEyhSF20OhQEukLCVVMWbmAQuKgVBpdBKrFp5y9JkiRJGpjC/i5A24bPfOYznHDCCZttf9/85jfZb7/9Ntv+trR58+atd51p06ax3377cc8992yFiiRJkrSp6l58hQLl3cHgKvWLGwmBUqr3s5QvDN+VhZlhxMDQFY184jNvEM7NUxbHRHTlfTFAFBHGMZVRzKuZFK3JkGIA6VKJdKnUtX+gWHrz+oZek+Svr8fdX1/6NAy9FPa4NmDUFbDbtZv8FkiSJEnSNisi2CwfA5HhoLQe559/Pr/85S/7uwxJkiRtZrWFDsrzay2MY8o6ciSKJRKFYq9tHtztUGaOmUCiFDGjvo7KQpHWREhnIkkxDMmHIYUwpBSGhHFMaxgwplCishRRUyyRimNScUy2VKKQSDDpN28ymEsEJBN85KEMcRzzzSdDvv90AhLdY5GytBX+9y/xureXJEmSJOlNGA5K6/HUU0/1dwmSJEnaAnKJcv6w12Gr5wEECAIK6SQBUL9oOWEp6loex7Sn05QSXWN5FlNJltcOY2FZhpkVZcQB5JIJComQfCKkM5kgjmOywKyKLPcPqeHpmgpWxY0BsDSdpGVxDgprtQ/GMUQrhx0NAoZdk+Kqf4WQDCGdgGzYvZcb/73up1zjGJ6cF7O03fBQkiRJktSTcw5KkiRJGpCeGT2ZjmwZYRwRx0FXYhfH7Nr8Om+kh0Eb7DBzPllKLB1ay/LRo5g4fyGZQoHZI4ZRSKdZmM1AqUQ+0XMCwDgIaE8lmVeR7V72amUZAO9obiMAksSQTUF7CTqKUJ7qmpNwzTxvZUC45tyHxDFUBNAZQQRDL2f1HIbJkLJy6CisDBOjmIow4rWz32L8UkmSJEnSgGI42E9WrFjBpZdeyjPPPMPy5csZOnQoRx55JGeffTaZTIbPfOYzJJNJPvKRj/DTn/6UBQsWMHbsWM4880ze+9739tjX7Nmzueqqq5g2bRqFQoFJkyZx9tlnc9BBB23Uev/4xz+45ppreOWVVxg8eDBnnHHGJp3rSy+9xJVXXsnzzz9PRUUFp556KnHc+wnmZ555hptuuokXX3yR1tZW6urqeNe73sXnP/95qqqqePLJJ/nc5z7HRRddxCmnnNJj2y9/+cs899xz3HfffSTW+sPMW7ntttu47bbbmDt3Ltlslr333pvzzjuPiRMnsmDBAk488UQA7r33Xu69916uvvpq9ttvPzo6Orjqqqv485//TGtrKwceeCBHHHHEJr1PkiRJ2roWVg3ruSAGCOgoz3LMnGeYkxnJkqrBPLX7ZFoqyqlpb2e31+cwYeFi8skEd++7L48NrycRhMT0Dt86w95dfXPLMuzZ0k4yiokCVgZ/K/9biiEdru4a7C5qrf0EAeSLUJGC1sLqiQ5joBDRUUxB+epbvbYo5sP3xkw9PtrId0qSJEmStD0xHOwn//d//8fLL7/MaaedRn19PdOnT+eGG26gubmZr371q0BXmHfRRRdx4okn8sEPfpB7772Xiy66iIsvvphjjjkGgJkzZ3LmmWd2h3jJZJIHHniAL3zhC1x88cUcddRRG7TeP/7xDz7/+c8zduxYzjvvPJqamvjRj35EEATU1tZu8HnOmjWLz3zmM1RXV3PmmWdSKBT4zW9+Q6FQ6LHeU089xec//3n23HNPzjnnHMIw5KmnnuKOO+6gpaWF73//++y///7U1dXx5z//uUc42NHRwaOPPsrJJ5+8QcHg/fffz/e//32OO+44PvzhD9PY2MjNN9/MOaH5tnkAALPZSURBVOecw5133smgQYP49re/zde//nX23ntv3v/+9zN+/HjiOOaCCy7gueee4/3vfz8TJkzgL3/5C9/97nc3+P3Z0oKBOZeqBpA1mygkbTivIQ106UKBXJQlDsMeF8Ijk/Zh93mzqMzluePdu9Ke7er+y6XT/G3v3RmxrJGyfJ4j/v08d00eT2NlOckoorjm76JxTEfYexaHTBRBEFAMYWFZpmthAFSmVtcQBlCMVgd+6xLF0JSHsgTko9UdhQDZtX4nDgMeXpggCAwH9fbizyFp03gNSZvGa0jSQGY42A+WL1/O008/zRe+8AU+8YlPAHDyyScTxzHz58/vXq+hoYH/+Z//4aMf/Wj3OqeddhpXXHEFRx11FGEYcskllzBo0CB++9vfUlbWNUzRhz/8Yc477zx+/OMfc9hhh5FKpfq83pVXXkl9fT3XX389lZWVABxwwAGce+65GxUOXnPNNQRBwHXXXcfw4cMBOOKII7rPaZXf/e53DBs2jKuuuopUKgXAhz70Ic444wyefPJJABKJBEceeSS33norDQ0N1NfXA/DII4/Q2dnZHZj21f3338+ECRP41re+1b1s55135ic/+QkzZ85kr7324n3vex9f//rXGTVqFO973/sAePTRR5k2bVqP/zcf/OAH+fznP8/TTz+9we/RljJoUDnJZN/DUmlbNnhwVX+XIG3TvIY0UOXTSRKlErlUkpiAMI4J45hSIsl17zqBvWfN6g4GVyklEiwcPIgJCxdTkc8xbNFynpxSy/Io5p3tHZTCkDCOKS8UqQgDykolOlaFhnHMLq0dXZ8HAY2ZdNfn6UTvv0olgjU6AuOeIWEAFFZ2FBZWz4n4VpKJgPp6r3W9PflzSNo0XkPSpvEakrZdsen+RjMc7AeVlZWUl5dz2223MXLkSN75zndSVlbGN77xjV7rrdkhl81m+eAHP8jll1/OSy+9xKhRo3juuef48Ic/TC6XI5fLda976KGHctlll/Hiiy8ybty4Pq03ZswYXnrpJU4//fTuYBBgv/32Y6eddqK1tXWDzjOKIp566ikOPvjg7mAQYNy4cRx44IH8/e9/71522WWX0dLS0h0MAjQ1NVFRUUF7e3v3smOOOYapU6fyl7/8hQ9/+MMAPPjgg4waNYopU6ZsUH3Dhg3jH//4B9deey3HH388I0eO5F3vehfvete73nK7J554gjAMOfnkk7uXJZNJTjnllLdVONjY2O6TT9ruBUHXL/HLlrWs72+iktbBa0gDXTGZpKMs2x3MlQiISxGJOKa8vYWZ9SNI5QsU0qke29W2tgHQnC7jwZ3HEEcxi9Mp7kwlGVws0hYm2K2zkykdnZy4aDkzKstoTybYoSNPXbFr+NFcGLAi9RYPcq0abpQAOvKQSa4OANvXGIWjGHcFiWvKl7rWXyWK+dXhORoa8hvzNklbjD+HpE3jNSRtGq8hbS0+pKe3I8PBfpBOp/nKV77CxRdfzEUXXUQ6nWafffbh8MMP57jjjiOT6RpeaPTo0T3CMoAxY8YAsHDhwu55+6ZOncrUqVPXeaxFixaRTCb7tN6qY40ePbrX6+PGjeOFF17YoPNsbm6mvb39Tfe3ZjiYSCSYP38+V199NbNnz2bevHksWbKk13a77747o0eP7g4HW1tbefLJJ7s7MDfEWWedxfTp07n22mu59tprmTBhAocccggnn3zyOmteZcGCBdTV1VFeXt7rnN5u/MVGA0Uc+/0ubQqvIQ1UUSLs1bEXhQGjFixl3xfmksmXKCRf51+7jmf22K6H3Sa9MZe6llaaMmXcsceutGVSsDLwi4OAhpW/Uy9KJZnSAWVRxPi2Tl4ry1K9cr1CAIuzaaop0VwKoCPqmmvwzZ7sKkXQnOsKC6NVcxCuGgcLglTA3vUxS9oCgmTAx3YpcNXLASsKAQHwjf1yHDUu8DrX25Y/h6RN4zUkbRqvIUkDkeFgPznmmGM46KCDePjhh3nsscd4+umneeqpp7jtttu44YYbAHoFg9DVjQcQhmH356eccgqHHnroOo8zceJEFixY0Kf1VoVxa3YWrn3cDRGs/ONGZ2dnr9fitX7i3nTTTVxxxRWMHTuWvffem8MPP5wpU6YwdepU7r///h7rHn300fzqV7+ioaGBJ598kkKhwNFHH73B9Q0bNoybb76ZadOm8cgjj/Dkk09yww038Nvf/pYrr7ySfffd903Pa3O9R5IkSeo/uVXDeq4hiGL2eeE1MvmuIC9VLLHv9JksGlJLW3mWeYPreGzcBIphght327HrL0lrzve3Uu3KIDACXivP0phK0RkGjOrM05oIqejMs0sTPFVV3hX4FSNYVydhMWJ4bcCitgR0ltYqFqhIsvjc4soFqycp/J/97RKUJEmSJK2b4WA/aG9v55VXXmHChAmcdNJJnHTSSRQKBX7yk59w880389RTTwFdHWpxHHeHbABz5swBujoIV80BmEwmOeCAA3ocY/bs2SxYsIBsNsvIkSP7vF4QBN3HWNOacyH2VU1NDRUVFcydO7fXa/Pmzev+PJfLcc0117Dffvtx5ZVXdnc6Avz85z/vte0xxxzDddddx6OPPsrjjz/OTjvtxMSJEze4vpkzZwKw//77s//++wPwr3/9i3PPPZdbbrnlTcPBUaNG8dhjj9HU1NRjHsaNeY8kSZLUf4phguSqcG+lIQ1NZPPFHuuFwNBlK3itoozWqkraMxmSpRJf+PcMbtpnV2ZWlrPmo2PVxRK7dnQSA2EU05hKMiSX5x2NzayK/3JhyKsBnH5IBb9+Pdu7azCOu4YMLZaYfmZEMYoYeV0aOgpdiWMAZBL85QM9a5UkSZIkaX3C/i5gIJo1axZnnXUWd911V/eyVCrFpEmTgK6uQIBly5bx5z//uXudjo4O/vCHPzBmzBh23HFH6uvr2XXXXbnnnntYunRp93rFYpFvf/vbXHTRRRSLxT6vV1tby957783999/PsmXLutebPn06M2bM2ODzDIKAww47jCeffJJZs2Z1L1+wYAGPP/5499e5XI7Ozk7GjBnTIxh8+eWXee6557prXWX8+PFMmjSJhx9+mGeeeWajugYBLrroIr7+9a9TKq1+Anvy5MmkUikSidVPba/ZpQlw2GGHAV3djqvEccxtt922UXVIkiSpf2Q6O0nmcpS1dZDKF6he0UI610Eh0fs2qbmqa0j5sBQRrvzdMM6kGBrGfGjJMk5oWM5BLW0c3tzKSU0rKIsBAgphQDKGXVtaWbMvMBNFDMkV+NGhrBwqtLeTRuVZsrIrMBnC7E/mKa9JQnUGqtJcekiJ3YduvvdDkiRJkrYlUbB5PgYiOwf7wZQpU9h777256qqrWLRoETvttBOLFy9m6tSpjBs3jgMOOIBf//rXJJNJvvWtbzFjxgyGDBnC3XffzeLFi7n88su793XhhRdy3nnn8fGPf5xTTjmFmpoaHnjgAV544QXOP//87s62vq53wQUXcNZZZ3HGGWdwyimn0NHRwe9+97seHXIb4txzz+Wxxx7jM5/5DB/96EdJJBJMnTqV8vJy8vmuoY6qq6uZMmUKd999NxUVFYwdO5ZZs2Zx1113dQel7e3tVFdXd+/3mGOO4YorriAIgo0OBz/xiU9w8cUXc95553HEEUcQxzH33Xcf+XyeD33oQ93rDRo0iGeffZY77riDgw46iP32248jjzySG2+8kYaGBqZMmcLf//53XnrppY2qQ5IkSf3jjC8N5ZELp/OeGS/yzxHjWJ6toLazjdnjhrLTrEXdT1LO3mEYywdVAVC3tJEwjomCgCiV4q/X7MC0/zTw/e80MT5fILXG8KIxMUEMYRxTWSz1On5NoUhb4U2Ki+EXx/YMDSvT8PonC8CbbSRJkiRJ0voF8dqTv2mraG5u5he/+AWPPvooS5cupaqqine/+92ce+651NfX85nPfIaFCxfyxS9+kcsvv5yGhgZ22WUXzjvvPPbZZ58e+5oxYwbXXHMN//znPykWi4wdO5bTTjuN448/fqPWe/HFF7nyyit54YUXqK6u5mMf+xgvvfQS//rXv7jnnns2+FzfeOMNrrjiCp599lnS6TQnn3wyAL/61a+YNm0aAIsWLeKyyy5j2rRpFAoFhg8fztFHH8348eP50pe+xA9+8APe+973du9zyZIlHH/88UyZMoXrr79+g2ta5d5772Xq1KnMmTOHKIrYZZdd+PSnP82BBx7YY52f/vSntLS08NWvfpXjjjuOYrHIL3/5S+655x6amprYa6+9OP300/mv//ovvvGNb3DCCSdsdE2by9KlLf1dgrTFBQHU11fR0NDi5OHSRvAakqBp3A+obe+aqa8UhCTjiKVV1fx+//dQ19xKU1UFTdXl1Da3UN3UQqYzTy6boamuhh0aGjjz38ezaFEjv79lBc8+U6AzmegOD8uKJVK18Hp7ijGdOVJxRDEMIYZUFNGUTvHjHw5l1E3rGFY0illyRu+5u6XtiT+HpE3jNSRtGq8hbS1DhlT1dwnbrc+c9spm2c+1N++8WfazLTEcfJtaFQ5uTBg3EDQ0NPC+972PL33pSz26/LSa4aAGAn+RlzaN15AEzTt+h6WZ0fxrhwl0pjMMb25k39dnMLeqjnsOPgSCgGxHJ+likY5sFojZY+YbTJy3iMd334n/ffxwFi1qpFhM8I3/XUQy7goaA7qmBvzuT0dw5FdXMLkjR1m8eqj6GGhPJPjB9+qZ8FvDQQ1M/hySNo3XkLRpvIa0tRgObjmGgxvPOQe1Tbr99ttJpVIcddRR/V2KJEmStmH/GrozT02YTGc6A8CimkE8tOs+/Gf0OMI4Joxj8tkMrZUVlJIJdpqzgBHLmpi2y0TCUasfxspkAr71w+FM3CNNIhMwcUqKi68YDsAfvlRGeo1gELrCw0ypRGUKuqLCNcQx/oVKkiRJkrSlOOegNkipVKKxsbFP61ZWVpLNZjfr8a+88kpmzZrF448/zimnnNJjHkLo6ijsi/LycsrLyzdrbZIkSdr2LK4a0atrL5fKUIiLZHN5OrOZ7uVhFNFQU8ny2kqmnDyEpTVP9dgukwk44+zBvY4RhQHhqnbCNSRWBoBXHpzn/MdXHmflegcM7xkmSpIkSZK0uRgOaoMsXryYE088sU/rbom599rb23nmmWd4z3vew/nnn9/r9WOOOaZP+zn77LM555xzNmttkiRJ2vaU1jGWSgw0V1RS2dZKslgkl0mTLJaoaG8nU8hz+l0HUCwW+dOf+naMiIB8GJBZoxswBnIrQ8lTd4o5codOjr8/RVNnwM/fU+CQkXYOSpIkSdJbidaenkF9Zjj4NnXttdf2dwnrNHjwYH72s5/1ad2JEydu9uN/6Utf4ktf+tKbvt7X2kaNGrW5SpIkSdI2rKZlOcl0yJLKoV0L4pgdl89iQd07GNzYQBAEVORyREFAFASccs6gDT5GRRpaUynyUUSmVCIOAjoSCRLR6gBwUBYef39hc52WJEmSJElvynBQGySTyXDAAQf0dxlv6u1cmyRJkt5+dmmczT4N/2Zm3URWZKoY3/QGNZ0reGL83pzy23dw95nTaIqzpEtFTj45Q9n7Nnyi+jAMgIDOZJLO5OpbsDAqbsYzkSRJkiSpbwwHJUmSJA1YURCSjCMmL3u1e1k+TJENI7KVSU6deuCmHyQISEURuTCxelkck4idV1CSJEmStPWtY4YNSZIkSRoYpg/bhXyY6rlsyK6M2Cmz2Y6RSgYkVw4pmogiklFEWSkijp1XUJIkSZK09dk5KEmSJGnAWjJoCL/b/UMcNOdpKvLtvDp4Ak9MOIDK5s07/195WUBbZ0wyAOKYEnD+2ZWb9RiSJEmSNJDEQdDfJWyzDAclSZIkDVhhqcSCwaP4w+D3dy8LgEJbabMe55KLB3PX3S389fE8mRT892erGDkyvVmPIUmSJElSXxgOSpIkSRqwiukUxWSS1vIyiokEmUKBqvZ2KisT6994A510YhUnnbjZdytJkiRJ0gYxHJQkSZI0YJV3dDJ3UC1x2DUdey6dJgoCRg8v9nNlkiRJkiRtGWF/FyBJkiRJ/aWudUV3MLhKIZVi+azOfqpIkiRJkqQty3BQkiRJ0oBV3t7Ra1lYinBae0mSJEnS9spwUJIkSdKANbRtOTVNrT2XLW5k0LB0P1UkSZIkSeqLKNg8HwORcw5KkiRJGrDidI6xry8in0rRXpGlvK2T5qGVJGoz/V2aJEmSJElbhOGgJEmSpAFrdvVOZNPtLBg0ljCKaR5SSUVrK8vn5/u7NEmSJEmStgiHFZUkSZI0YI1fNpc3Bo+mrqmFEYuXU9HWSZROUlbsPRehJEmSJEnbAzsHJUmSJA1Y2UQ7+z77GoMbu+YdjGbM5V97jCccW9u/hUmSJEmStIXYOShJkiRpwEq2Bd3BIEAYwy4z5rGisdiPVUmSJEmStOXYOShJkiRpwMp3lpNda1lZrkBdedQv9UiSJEmS+iYm6O8Stll2DkqSJEkasDpSZb2XpZOUBvVeLkmSJEnS9sDOQUmSJEkDVlNtOZQCqjtyJOKYXDJBS0WGXFvc36VJkiRJkrRFGA5KkiRJGrDmD6sjm1tKPhUCAYkg5pVJo9jlHTX9XZokSZIkSVuE4aAkSZKkAWto63L+uccEKjs6SBUiWirLqFrWyt4nD+/v0iRJkiRJ2iKcc1CSJEnSgHXoHw5h7PyFBAXIJZIMamhlxKQEiaQT20uSJEnS21kUBJvlYyCyc1CSJEnSgJUZUsn7/vZeXv/+UzTPbWfS1w+gfERlf5clSZIkSdIWYzgoSZIkaUALw4AJXzmov8uQJEmSJGmrcFhRSZIkSZIkSZIkaYAwHJQkSZIkSZIkSZIGCMNBSZIkSZIkSZIkaYBwzkFJkiRJkiRJkiRtU6Ig6O8Stll2DkqSJEmSJEmSJEkDhOGgJEmSJEmSJEmSNEAYDkqSJEmSJEmSJEkDhOGgJEmSJEmSJEmSNEAk+7sASZIkSZIkSZIkaUNEQX9XsO2yc1CSJEmSJEmSJEkaIAwHJUmSJEmSJEmSpAHCcFCSJEmSJEmSJEkaIAwHJUmSJEmSJEmSpAHCcFCSJEmSJEmSJEkaIJL9XYAkSZIkSZIkSZK0ISKC/i5hm2XnoCRJkiRJkiRJkjRAGA5KkiRJkiRJkiRJA4ThoCRJkiS9iRcb4BcvhBSj/q5EkiRJkqTNwzkHJUmSJGkdhv4yDSvnsPjqU/CuYQVuP8GUUJIkSZK0bTMclCRJkqS17PbrBMSw5vz2jy1KEsd5Aue8lyRJkqR+F3tzttEcVlSSJEmS1rI0n2BdKeCH7kn0QzWSJEmSJG0+hoOSJEmStLY47r0sCGh8denWr0WSJEmSpM3IcFCSJEmS1raObBAgm+/cunVIkiRJkrSZGQ5KkiRJUh9Nqx3Ka839XYUkSZIkSRvPcFCSJEmS1ha+ycT2qQoO+H2anW7MbN16JEmSJEnaTJL9XYAkSZIkvf28STgIEAS0RQkW5bJbrxxJkiRJUg/RW9y26a3ZOShJkiRJGyoI+Erj8f1dhSRJkiRJG8xwUJIkSZI2RtHbKUmSJEnStse7WUmSJEnaGN5NSZIkSZK2Qd7OSpIkSdLa+jJ3hXdTkiRJkqRtULK/C5AkSZKkt524vwuQJEmSJL2VKOjLU51aF591lSRJkiRJkiRJkgYIw0FJkiRJkiRJkiRpgDAclCRJkiRJkiRJkgYIw0FJkiRJ2ijObyFJkiRJ2vYYDkqSJEmSJEmSJEkDRLK/C5AkSZIkSZIkSZI2RORoLhvNzkFJkiRJ2ihxfxcgSZIkSdIGMxyUJEmSpI3iU6qSJEmSpG2P4aAkSZIkbZS+dw623DeHVyZM5ZUJU2l7eH738qVX/4eG387cEsVJkiRJkrROzjkoSZIkSWtLBF2NgVHclQEGQdejlcU1A8G+dQ7O+chDFB9eTDbq2nbRaY8Q7FJJ6oVGKmkhR5rXL3iKoY8eT/mk2s18IpIkSZIk9WQ4KEmSJElri4Ew6AoJVylFG7WrxmeaGBStDhVTpYj0S/OZzH9IUgKggaG8/u57GLfkE5tStSRJkiQNGCVnethoDisqSZIkSWuL49X/jeKuYHDjskFShVKvZRNLM7uDQYB6llBD08YdQJIkSZKkDWDnoCRJkiStLQaK0XqmFezbY6ph1HMnASXK6Oi1Xjltfa9PkiRJkqSNZOegJEmSJK3LWwaDrO4uXI9Mvgh0NR5GQEyCFVT3Wq+Nyg2rT5IkSZKkjWDnoCRJkiRtjCDR51Vnjx7MksGVBMDQhhbC+TuzG/8mRYEYWMpwWqilbosVK0mSJElSF8NBSZIkSVpD+Revh50/A4n1hX99G1Z04bAaFg/p6hSMgUVDa8jkC0RL911jHwEhxY0tWZIkSZKkPnNYUa3TN7/5Tfbbb79+reGaa65hv/32Y8GCBf1ax7q8nWuTJElS31R8+qcMHvkpqk68uGuI0MYW6od+koqbHuG8px6kLJ9b7z6C7+X5wiNv/sxl6w/+SmN1ea/l2c4iXbdjAasCwpiQ6I7pG3k2kiRJkjSwREGwWT4GIsNBrdMHPvABvv3tb/d3GZIkSdIWUT/0k5TfO42wGJN96lXqh32K+knnd/fxXfXHG3njkvPZd+7MN9/JyikHfz87ze2v9Hyp80eP07bTT6j+8f1U5dt7bVrT1rGOHQbMO+dftA374UadkyRJkiRJfeGwolqnPfbYgz322KO/y5AkSZI2jygi9ehLBK0dVJ7x014vr+tZ0SHtLVx+3028+5xvrXufASsftwz5x3eeZvIj/2F4ewsFknRSRYJqRtPGmEVLWVJdQymZICxFhFFEIioBvYctHcE8lsVDydz3Esn37bIJJyxJkiRJ0roZDkqSJEnargUr2qn54A9I/fv1Dd523/mzSRcL5JOpN9l5AGHAPXvvzXfuf4QUxZUDhS6mmgYSwN6556h+oYWG5FAyhRIJSmTooIMKVseSMRCQosSOzKDhUwtILvn6Rp2vJEmSJElvxXBwG/PCCy9w7bXXMn36dBKJBFOmTOFzn/scO+64IyeccAIHHXQQe+21FzfccAPz5s1j2LBhnHbaaZx66qnd+1ixYgWXXnopzzzzDMuXL2fo0KEceeSRnH322WQyGaBrzsF7772XadOmbVBtn/rUp/jv//5vPv7xj/d47Zvf/CZ//etfefDBB8lmszQ1NXH11VfzyCOP0NTUxIgRIzjxxBP5xCc+QSLR+wlq6Jrn7xe/+AV33303I0eOfNPl11xzDb/5zW+48cYb+dGPfsT06dOpqqriIx/5CKeffjq/+c1vuOWWW2hpaWHPPffky1/+co/9LV68mJ/97Gc88cQTtLe3M27cOD7xiU9w7LHHvuX5f//73+e2227jf/7nf/joRz/a5/dtSxqgwyVrAFn1Pe73urRxvIY0UJT96i8bFQwClBULvHHJ+Rz9qS8zfeS4N13vmJdfIU0RgIAStSzpjv0SRCRiyBZKAEQk6KCSOhbTQi0AQ1i8MjTsJEFMBa3EK9qJa3rPVyhtL/w5JG0aryFp03gNSRrIDAe3If/85z/57Gc/S319PaeffjrZbJabb76Zc845h5tuugmAJ554goceeogPf/jDDB48mNtvv51LLrmEkSNH8q53vQuA//u//+Pll1/mtNNOo76+nunTp3PDDTfQ3NzMV7/61Y2ub8qUKYwePZqHHnqoRzhYKBR4+OGHec973kM2m2XFihV8+tOfZuHChXzwgx9k7NixPPXUU1x55ZW8/PLLfO9739u0N2rlMc877zwOPfRQDj/8cO6++25++tOfMm3aNBYsWMDHPvYxmpqauPHGG/nWt77FNddcA8DSpUv51Kc+RRzHfOQjH6GqqopHHnmEr33tayxdupTTTz99ncf7+c9/zm233cZnP/vZt00wOGhQOcnkuoNWaXszeHBVf5cgbdO8hrTdm714oza7c9f9+PEhx1ORz/H5J/7EWR86dx1rdXX8vXvWa91LEhR6DVO6YmUIuKaQgMm8tMaeYoKVfYeQoK6xjWDisI2qXdqW+HNI2jReQ9Km8RqStl2R4f5GMxzchlx++eXU1NRw0003UVtbC8DBBx/MKaecwq233gp0db397ne/Y6eddgLg0EMP5dhjj+VPf/oT73rXu1i+fDlPP/00X/jCF/jEJz4BwMknn0wcx8yfP3+Tazz22GP55S9/yaJFixg+fDjQFVi2trZ2d97deOONzJkzhx/96EcceuihAJxyyin84Ac/4NZbb+W4447rDjI3VrFY5Nhjj+W///u/Adhnn3049dRTmT59OnfeeSeDBg0CYOHChfzpT38in8+TTqf52c9+Rj6fZ+rUqdTX1wNw6qmn8v/+3//j6quv5vjjj6eurq7HsW655Rauu+46Pv3pT/PpT396k+renBob233ySdu9IOj6JX7ZshbiuL+rkbY9XkMaKLL7TKDy5kc3aJvvHXoSX3nfR7sfJX9w0p5QWseKK1+fX7X6j0olUisjw9XS5MjRswswTa7nrgjIkyRNnhwZlg2vhYaWDapb2pb4c0jaNF5D0qbxGtLWUl9vAK23H8PBbcTy5ct58cUX+fjHP94dDAKMHTuWX//61wwfPpyHHnqIsWPHdgeDAPX19dTV1bFs2TIAKisrKS8v57bbbmPkyJG8853vpKysjG984xubpc5jjjmGX/ziFz26Bx988EHq6urYf//9Afj73//O+PHju4PBVc4880xuvfVWHnnkkU0OB4Ee+x8zZgwAe+65Z3cwCDBy5EjiOO4eXvXhhx9mv/32I5lM0tTU1L3e4YcfzgMPPMA//vGPHsOL3nfffVxzzTWccMIJfPazn93kmjc3f7HRQBHHfr9Lm8JrSNu7jo8eQvIfr5L5w5MEcdwruFuX7x3+/h5jTMVhCHEE0VorxkApYnmicmWwVyQmQRuDqKCRYOUqg2hgETt0HzlNJ7U09thVnhTtlBMRU047+Uyqa2NpO+fPIWnTeA1Jm8ZrSNJAZDi4jVi4cCEAO+ywQ6/XJk+e3P35msHXKul0mlKp1P35V77yFS6++GIuuugi0uk0++yzD4cffjjHHXdc95yDG2vs2LHsuuuu3eFgZ2cnjz76KMcffzzJZNe324IFCzjooIN6bVtfX09VVRWLFi3apBpWGTx4cPfnq4699vsThiEAURTR1NREa2srDz/8MA8//PA697l2bVdffTVhGPLiiy9SLBa7jyNJkqS3kVSSlqvOoe3rpxK05yj7yA/Jvt7wlgFhe3pdvxcHhFGRfea/xrSxO3YFhaWuvyQ9O6aOv0zehb1nzIUEvDpkF8pKOd679O80Uc8ixqzcR4kMHeQop4la6lhOQFcw+BoTqWAZIeUEf1z3cPaSJEmSJG0qk4xtRBR1PaIcrGecyPW9Dl3dfQcddBAPP/wwjz32GE8//TRPPfUUt912GzfccAPpdHqTaj366KO57LLLWLhwIS+++CLt7e09uu3it3gUJ4qiDQ7YVr03a0skes+391bvz6r9vPe97+UDH/jAOtcZNWpUj6+PPfZY9tlnH77zne/w61//+m01rKgkSZJ6ioZ3PSjW+vSPSY05k0Rnsbuzj5X/DVd+XtXRTlPlGsP/xDE7Ll3ABY/dxwUnfwoSIaz6PTSOufpb4xlRMb579aHzlhNe/jBtv65gMcPXqCJBjgrKaWYBo1jIaBIUKZKmGMC4eAXByCwd7xiPJEmSJElbguHgNmLV/H3z5s3r9dpPfvITqqur+7Sf9vZ2XnnlFSZMmMBJJ53ESSedRKFQ4Cc/+Qk333wzTz31FIcccsgm1Xr00UdzxRVX8Mgjj/Cvf/2L0aNHs/vuu3e/PmLECN54441e2zU0NNDW1tZ9rmtbFfbl8/key1cNmbqpamtryWazFItFDjjggB6vLVq0iBkzZlBWVtZj+XnnnceIESO45557uO666zjqqKMYPXr0ZqlHkiRJW07jnOsIXp5P+Y/vpOPMI4kO2BnimPphnwIgt6orcGVaOKqxgbErlnH+B85cOcRo1/hTQQB3HNfOiIq1bq1G1xH96AO0nXYAhWMfXOvoAS1BLYm4K5As0vVwXhxD5/xvQ6r3Q26SJEmSJG0u4fpX0dvBkCFD2HnnnXnggQdobW3tXj5v3jxuueWWPgdks2bN4qyzzuKuu+7qXpZKpZg0aRKwepjNTVFfX89+++3HX//6V5544gmOOeaYHq8fcsghvPbaa72G7rzxxhsB3nS+wVXDhL7yyivdy9ra2nj88cc3uWboGnr04IMP5rHHHutxDIBLL72UCy+8sMc8hKsEQcCXvvQlisUiP/jBDzZLLZIkSdry4kmjaLv2v7qCQYAgoGHJjSx74nt0ZMshiqHY9TG/pp6/7LzH6mAw6goHoy9nOGDYmx8jte8oonUMYJqI1zH6RRgYDEqSJElSH0UEm+VjILJzcBvyP//zP5x//vmcfvrpnHzyyYRhyNSpU6mqquKTn/zkm86Tt6YpU6aw9957c9VVV7Fo0SJ22mknFi9ezNSpUxk3blyvjrmNdeyxx/Ktb30LoFc4+KlPfYq//vWvfPnLX+ZDH/oQY8aM4emnn+Zvf/sbhx12GAcffPA693nooYfyox/9iEsuuYSFCxeSTqe54447enXzbYrPfe5zTJs2jbPPPptTTz2V4cOH89hjj/Hoo4/ygQ98gIkTJ65zu8mTJ/OBD3yAW2+9lQceeICjjz56s9UkSZKkrSvecSQ8vNYNYhQDMQRBV7tfxOrxSNejhiaaqF/vesk3GS5fkiRJkqTNyc7Bbch+++3H1VdfzbBhw/jFL37BDTfcwOTJk7nuuuuor1//Hxugq8vtRz/6ER/84Ad57LHHuOSSS7j99ts5/PDDufrqq0mlUpul1sMOO4xMJsPkyZMZN25cj9dqamq4/vrrOe6443jwwQe57LLLeP311/nCF77A97///Tfd56BBg7jiiisYPXo011xzDTfddBNHHnkk55577mapGWD06NHccMMNvOtd7+KOO+7g0ksvZf78+VxwwQVcdNFFb7ntZz/7Werq6rj00ktpaWnZbDVJkiTp7SLo6u5LrPwIS33aqoJmhrCAkBIhJYYyv2tfvfZuOChJkiRJ2vKCOI77+LyrpG3J0qUGlNr+BQHU11fR0NCCP82kDec1JL25ob/MrPuFBLBqKP5CgfjLZSxa1Egi8eaDsiwadg27xM8Trgz/SiSZzv691gspMnrJpzaxcmnb4c8hadN4DUmbxmtIW8uQIVX9XcJ265iz52+W/fzpF6M2y362JQ4rKkmSJEl9VQJKESQD+jquaGtYySul3RnCQhKUWMYQAkrE9JxfMKRvnYiSJEmSJG0Kw0GtV0NDQ5/WKy8vp7y8fAtXI0mSJG0NMesa+rNbceUchH1QEbfQQSVz2LF7WSUNtFJH10wPMSElFlaXM3JTSpYkSZKkAaQUvMU9m96S4aDW65hjjunTemeffTbnnHPOFq5GkiRJ2grWkw126duNaMXvjyd3yl10xF3DCVWznMw39qP5sheoa2klCkJm1o1mn+kf2qSSJUmSJEnqC8NBrdfPfvazPq03atTAG5dXkiRJ26nN+ARq9SGjiRecx/Iv/ZVkeyfVP/kYQSrB+P96F3EUQwDv8IlXSZIkSdJWYjio9TrggAP6uwRJkiTpbajvgV6QCBn84yN6Lw8NBSVJkiRJW1fY3wVIkiRJ0rapb3MOSpIkSZL0dmI4KEmSJElri/sS/BkOSpIkSZK2PQ4rKkmSJEmSJEmSpG1K5CwNG83OQUmSJElaW9CXu0xvpyRJkiRJ2x7vZiVJkiRpo0T9XYAkSZIkSRvMcFCSJEmSJEmSJEkaIAwHJUmSJEmSJEmSpAHCcFCSJEmSNkpf5iWUJEmSJOntJdnfBUiSJEnStinu7wIkSZIkacAq+cDmRjMclCRJkqS+CIDkysFX4hiK/VqNJEmSJEkbxWFFJUmSJKkv1rx7CgIcVlSSJEmStC0yHJQkSZKkvlhzFNE4poyOfitFkiRJkqSNZTgoSZIkSX0R0TWcaBRDMeLS+nv7uyJJkiRJkjaYcw5KkiRJUl8VYyDmsBEFMnHU39VIkiRJ0oBVcqaHjWbnoCRJkiT10YS2xSw+M89vjy70dymSJEmSJG0Uw0FJkiRJ6qO2mjoCn06VJEmSJG3DDAclSZIkqY9ik0FJkiRJ0jbOcFCSJEmSeonXufT4HZ1nUJIkSZK0bTMclCRJkqS1HDK8uM7l3z943aGhJEmSJEnbCsNBSZIkSVrLbcdHVCZ6dgl+b/98P1UjSZIkSVpbFASb5WMgSvZ3AZIkSZL0djT7jAJxDMUYUj5WKUmSJEnaThgOSpIkSdKbCAJIDcwHSSVJkiRJ2ymff5UkSZIkSZIkSZIGCMNBSZIkSZIkSZIkaYBwWFFJkiRJkiRJkiRtU0qBc0BsLDsHJUmSJEmSJEmSpAHCcFCSJEmSJEmSJEkaIAwHJUmSJEmSJEmSpAHCcFCSJEmSJEmSJEkaIAwHJUmSJEmSJEmSpAEi2d8FSJIkSZIkSZIkSRui2N8FbMPsHJQkSZIkSZIkSZIGCMNBSZIkSZIkSZIkaYAwHJQkSZIkSZIkSZIGCMNBSZIkSZIkSZIkaYBI9ncBkiRJkiRJkiRJ0oYoBUF/l7DNsnNQkiRJkiRJkiRJGiAMByVJkiRJkiRJkqQBwnBQkiRJkiRJkiRJGiAMByVJkiRpHfLPN9B80G9YvvN1tFz/n/4uR5IkSZKkzcJwUJIkSZLWkvvLHHjvDVTPWsCgpmVk/++PzJ90U3+XJUmSJEnSJkv2dwGSJEmS9HZTPO0PFMkyn2oABtPMoMYG2qc3UL5HfT9XJ0mSJEkqBv1dwbbLzkFJkiRJWkMcx3SQ4XVGsoJKVlDJa4ykSJKlH7q/z/t58cEGfvXp/3DjZ/7DvOdbtmDFkiRJkiT1nZ2DkiRJkrSGzj++TuPKjsHVApZTQ9DU3qd9XHf2CxRaVz+Nefd33+DgTwxlz/cN3ay1SpIkSZK0oewclCRJkqSV4nyJjs89RCepXq/lSJJPVb7l9m+80MKln3mZztaeN1tJ4K+/bdi8xUqSJEmStBHsHJQkSZKklZZOuZGFbXVA78krxjKDzprhb7rtjT9dQMcTy6kAImBheRlhHDO0o5MAyEbRlipbkiRJkqQ+MxyUJEmSpJUWN1WydjAYUGJ3plFFM81v0TnY/sRyEkBrMskDY0ayIpMGoL6jk6PmLCBlOChJkiRJm01xHQ91qm8MByVJkiRppYhEr2UxCRoYRkRAsam0jjWgra3UPYzoP4fU0ZJOsdfS5ezYvIJiGLKwvIxBudwWrV2SJEmSpL4wHJQkSZKklVIUKJDutXwB44jJ0pgexKR1bBcUSt3PrC7LZth9WSN7Nyzvfn1QLk9z0tsvSZIkSVL/C9e/iiRJkiQNDJV0rvws7vVaG5UQrvsWqpiPKa38fGhHJzs2tfRap6JY3ExVSpIkSZK08QwHJUmSJGmlNDkgJkvvIUDTFEgHvUNDgExVgpZUV2fgvkuWUVbqHQQW3yRYlCRJkiRpa/LuVJIkSZJWWkYtEFBJG0F3L2DXcKNDaCRc0bHO7YodEVWFrkAwE0Wko94hYkdiXbMVSpIkSZK0dTnphSRJkiStVFx5i9TAYAACIrLkGEITKUqEpdI6t4uJ1/vkZbnDikqSJEnSZlMI1r+O1s1wUJIkSZKAuNgz+MuQYwTLyJKnnSxLqCXHuu8+w9T6B2VJxeseklSSJEmSpK3JcFCSJEmSgNzCFlgZ/oUUmcACUiuHFk3TSgdpWhN169y2oy0iHwRMH1LHvMpyKgpF9mpoZGhHZ/c6hdDHWiVJkiRJ/c85ByVJkiQJgIgEBQDSFFlBBWv2+pWRZ3B52zq3bG8r8o/hQ3hh8CCaMhnmV1bwwJiRtCa7nsdsSSaZWVO1pU9AkiRJkqT1MhyUJEmSJCAMgu4bpE6yzGcocxjW/XoMFFd0UGzO99o2HwW8VlPFDi1tHLBoKbssbwLg9epKAMqKRRaXZ7bwGUiSJEmStH4OKypJkiRJQMfzzRRI9VjWTBU5lpGhSJ4Eg2ln0U7XM3jOp3tu21xgnyUt7L4yFATYqamFpdk00HXjNbK1Y0ufgiRJkiQNGIXAqRs2lp2DkiRJkgTk2+N1Lo9W3jYlyQEwnEZeP+3xHus0LSuw6xrBIMDgXI4RbasDwapCaTNWK0mSJEnSxjEclCRJkiQgOzgkQc8AL0OOLF3DiFbRxFBepYIGyh9/pcd66UFpEuvY54y6WqBrSNJSvO7wUZIkSZKkrclwUJIkSZKApicbqKIFgARFaljBeBYSACF5ylgGQDkNpGnvse2QuiSN6Z5DkhaDgNdqKlmWTtOSTJKNoq1yHpIkSZIkvRXnHJQkSZIkIOiMqWMFTdRQyQr25XFyVBMQkaGFgK7OvwCooK17u99etoj2Z5t4bfAgmvJ5RrZ1sCKdYtrQwXQkEmRLJcpLJZZmyvrpzCRJkiRJWs1wUJIkSZKAinfWs+LnlSQo0kwdc5jAWGay9hT3ESFZYhKNEcuXdNLxbBMhsGPTCv6w0zgIVm9Rni/QmUxQUSoxoiO3NU9HkiRJkqR1MhyUJEmSJKBsSg3NhJToGh50JrsxlwkMYx7jmEmaPJ1keYXdSQK7fmU+v3jl1e7w8I2aqh7BIEAijrl7whhGtLXz7nmLtu4JSZIkSdJ2rNDfBWzDDAclSZIkCYjDkBraaaa2e1mOMhYxlrlMIE2OPFliQhIUSec6WDG3nTK6hhrtSPa+vepMJgBYWFHOrJqqrXMikiRJkiS9BcNBSZIkSQKiGKpoJ0WBAimqaWRnXqCKZhoZzMvsTkwIQIkkGYo0lGcZnWvn+cG1DGnvgMG1PfY5pqWNSY3NzK8sp5BI8NSTLRx4kCGhJEmSJKn/hP1dgCRJkiS9HbTf8gotVDCB+dTRxN48SS3LSVCiniXsxT+AGIAEJbLkeHbkUJ4aPoT/1NUyvrWNgxYuobxQJIxiJjS3cODiBgYVCkxobWPfhuVcdVcnnS1FrnspZOxvsoz9TZYbX/a2TJIkSZK09dg5KEmSJGnAa71uOqO//yumcSijWMY4Xie11gwWFbRSyQpaqWIkSykRU9vSxsyVw4UWgoDJTSuY3LTi/2fvvsPsKuu9jd9rt9nTa3ovJJEaIEJIEKRJUIoKhHZUUJpHASsW5AgKvhxROUhQQRBERCkHRDwqvYaWgBTpJJDeJpPpM7uu949JJpnMhCQkZEjm/niNZD+r/daeWdeeNd/1PE/nNo+PHMjDYweTjkUZ0NTKiS/M4cybi/lnUQmsnq3w208nmLUsy4wDstvsfCVJkiRJfZePqPZhZ555JkcddVRvl7FVXXTRRUyaNKnPHFeSJElbR/i9e4AYeaIsYABvM7z7OkCGBEWkiJElRgvjVzUSALlIhNcry7qs31gQ59Exg0ivnndwWWkRd+42ioWrQtYEgx0Cbntn7XOb6RzMaQzI5bf6aUqSJEnSDqM1CLbKV19kz8E+7Itf/CLt7e29XYYkSZK0TWVWpqg99zFyrRlWvZWHlhwDSPICU4iRA0KaKWcl/ahmRed2ixhBikIAssQopI13ykoZV9/Ay9VVJPJhl+OUpTJ8YfabXLPfzp1tCypKqC+Ib7C2Y++N8fjSGB3hYcjnd8rwsyk5ANpz8M95AWc9XsCaI10wMc15e5giSpIkSZI2neFgHzZ58uTeLkGSJEnaZsJ0jvpb32bVN5+mlDYaKSNPkgg5kiSYwHxCIE/APAaQpYZlFJMiQj1VLGdw577SxIlQwMjlS3ltxHDmDy2jct6Cbscc1tDC4IYWFpcXA1CYztKS6Pk2bPYy1gkGAQJueivOxXvn2P1/C2jKrG1f49IXEhwxop3iOAwp3gpvkiRJkiRph2c4KEmSJGmH1v7APOae/DQZYkTJM5xGllNGO0kACkmRJA10xG5RQoawgncZTBPFdB0CtMMbg4fwzuhJDElHaV3VTEk6y9LSQvq3dB+ZIxJ29POL5vN8+pV3GdLQzDWTd6Ypmeiy3if/mezhWAGj/5wg3OCMEAH7371mu5CdK/LsXBXytd2yjKsIN7CNJEmSJKkvMxzczhx11FEMGjSIa6+9doPtRx11FPvttx8TJ07kxhtvZOHChQwYMICTTjqJ6dOnd25z5plnsmTJEu65557OtlmzZvHrX/+aN998k+rqar74xS/yyiuv8NRTT3VZb1NcdNFFvPDCC/zoRz/i8ssvZ+7cuQwcOJCTTjqJ4447rsu6s2bN4g9/+AOvvPIKzc3NVFVVsf/++3PuuedSWlrKU089xTnnnMN3vvMdjj/++C7bfu973+P555/n73//e491LFmyhF/96lc89dRTtLa2MmLECKZPn85nPvOZzaphjddee40ZM2bw8ssvU1xczPTp0wlD//AiSZL0YfXmybMI6RjKM0eEFHFaWNvNroUi3mYo45hPjAylLKGAJqopZA4fYRlD19lbSFtxnDcmDIPVc1PstLKRz778Dn/eYzTjVzRQsM5kgXUFCaItGabWLma/hSuoaO8IIQ+cs4S/7TJinf1ueJ6LDQeD628b8Gp9lFfr4cFFUZ78dDvVyY1sKkmSJEnqcwwHd1BPPvkkDzzwACeccALV1dXceeed/PSnP2Xw4MHsv//+PW4za9YsvvrVrzJkyBDOPvtsVq1axU9/+lNqamredx0NDQ2cc845TJ06lSOPPJKHH36Yyy67jKamJk477TQAnn76ac4991z22GMPzjrrLCKRCE8//TR33XUXTU1NXHbZZeyzzz5UVVVx//33dwkH29raePzxx/n0pz9NNBrtdvxFixZx6qmnkk6nmT59OtXV1Tz88MNceumlzJ8/n/POO2+TawCYM2cOZ555JmVlZXzpS18ik8lw8803k8lk3vd79EHqo3Opqg9Z8zPuz7r0/ngNqS9o/sucLuFaNQ0UkoL1ArcMcRooYSSvkKQJgCJa2ZXnaKGUZspXrxmwqqa024UzdmUj2WiUXxywO6c//SqxHCwtLuSFmkpiWVgWSxJdJzTcfclKXhxcxYLyEohs/YtwVSrgnnlRTpuQ2+r7lrYWP4ekLeM1JG0ZryFJfZnh4A5q2bJl3HLLLey0004AfPzjH+eII47gn//85wbDwSuvvJKSkhJuvPFGysrKANhjjz34xje+waBBg95XHU1NTZx00kl885vfBOC4447jy1/+Mtdddx3HHnssZWVl3HLLLQwYMIBf/epXxOPxzvVOO+00nnrqKQCi0SiHHXYYt99+O7W1tZ2B5aOPPkp7ezvTpk3r8fgzZsygoaGBm266iQkTJgAwffp0vvnNb3LzzTdz5JFHMmbMmE2qAeCaa64hCAKuv/56Bg4cCMChhx7KySef/L7enw9SZWURsVj3wFTaEVVXl258JUkb5DWkHVlQUdTldSmtRMkDIev31guBgtXBYOf2QAW164SDkGjPdjtOUyJGLhLwmZffoSiT47adRnXddxCwqLiICfWNAMwcNZAFlZt67b2/v1iVliSpqdlYr0Op9/k5JG0ZryFpy3gNSduvNsP9981wcAc1YsSIzmAQoKamhqqqKlauXNnj+rW1tbz++uuceOKJncEgwAEHHMDo0aNpa2t737Wceuqpnf+ORqOceOKJnH/++Tz77LMceuihXHHFFTQ1NXWGcgD19fUUFxfT2tra2TZt2jRuvfVWHnzwQU444QQA7rvvPoYMGcKuu+7a7bi5XI6ZM2cyefLkzmAQIBKJ8MUvfpHHH3+cxx57jDFjxmxSDfl8nqeffpqpU6d2BoMAI0eOZPLkyTz22GPv+z36IKxa1eqTT9rhBUHHL/ErVzbh6L7S5vMaUp/w8UGsGwSmiVNGK3GyZIivs2JIGa3kiRGla/jXQHWX11XLm1na0E5L+eoxO8OQdDTKqc++zuhVzeSCgEQuR3q9kS1KMxlC4NX+FcwcOZBN1z3I3JjqgpCDa1qord2szaRtys8hact4DUlbxmtI20pNjQG0PnwMB3dQlZWV3doSiQS5XM/DCi1duhSAoUOHdls2atQoXn311fdVR3l5OdXVXf+YMnz4cAAWL14MdASGixYt4je/+Q1z585l4cKFLF++vNu+dtttN4YOHdoZDjY3N/PUU0/xuc99rsdj19fXd84x2NM5Qcd8hJtaQ0NDA62trT2+RyNHjvzQhYOAv9iozwhDf96lLeE1pB3dTrdPZs7xTwIh7cQIgXHMZx4DaSNJnCxDWE6aOO8wnrG80rltCyU00/VmPhKGfOT5BSwdVkFjZRGNlUVUt6UoTmfIBgHxMGTiijqeHdivc5sgDHls0ADCAN4aXkk2un6PvjUXYfcQMEqO3HveuoWdW+5dk2enipBzds1SWeC1re2Dn0PSlvEakraM15CkvshwcAeRz+e7vA7eZ5exsIdPwkQi8b72BRCLdf8RWxNQRiIdfxD5wx/+wJVXXsmIESPYc889Ofjgg9l111259dZb+cc//tFl28MPP5wbbriB2tpannrqKTKZDIcffvgmn8saa96vNT0FN6WGNe9pe3v7Zh1LkiRJvavowCHstvx4wlyettveouG8+ymnldEs6VwnRYx3GUyaGFEgTjNNlLGUYYR0H6o9ms/T1C9GY3kx7dEIjw4eyOKSIiL5kPJUipZ1RqQgDAmDgLZ4jBVVRWQ2MPT7fZ9s5xN/T9I1IAyZe3KGg/4aMLd5TaDYdflLx7XTrxC65Y2SJEmSJPXAcHA7E4lEyGQyXdqy2Sz19fUMGTLkfe936NChBEHA/Pnzuy1btGjR+95vXV0dra2tFBWtnetlwYIFQEcPwlQqxTXXXMOkSZOYMWNGlzDx17/+dbf9TZs2jeuvv57HH3+cmTNnstNOOzFmzJgej11ZWUlhYSHz5s3rtmxN24ABAza5hvLycoqLizvrX9fChQs39lZIkiSplwXRCEUnjadg2giav/EokdY2St58AxrTNDQlSZGgijrmMIaND+MZsN8rr3D7AR/nhX5VLC7p+H03HwlYVZhcb9WOfY1qbmbuiMoNPpo+sR8cPCjLQ0tiq48fcvr4DIVxePrYNAAPLwo48YGCzn6GF+2dZmDx+3o7JEmSJEl9lOHgdqa6upp3332X9vZ2ksmOPzo89thjpFKpLdpvRUUFe++9N3//+985/fTTO4cCfeWVV3jppZcYNGjQ+9pvGIbcdtttnfMOZrNZbrnlFkpKSthnn31IpVK0t7czfPjwLqHcG2+8wfPPP9+5zZplo0aNYvz48TzyyCO88MILnHbaaRs8djQaZcqUKTz88MO8/vrrnfMOhmHI73//e4IgYP/999+sGg466CD+8Y9/MGfOnM5QcvHixcycOfN9vT+SJEna9qKVScpu6Dr6RGX//2I5g1hFBZs2v19Ioi3PvMpiFhcXbcLqIenE6h6D+RCiPR/jz5/Iks1nWdICQ0ogst5qBw0JWfaF7iNZSJIkSZK0qQwHtzOHH344l19+Oeeeey5HHHEECxYs4K677nrf4d26vvWtb/GlL32JU089leOPP550Os0f//hHCgoKtmi/1113HUuWLGH06NE88MADvPTSS1x44YUkk0mSySS77rorf/3rXykuLmbEiBHMmTOHu+++u3PY0dbWVsrKyjr3N23aNK688kqCINjgkKJrnHPOOcyePZuzzjqL6dOnU1NTwyOPPMKsWbM45ZRTGD16NMAm13D22WfzxBNPcOaZZ3LyyScTjUa59dZbKSoqIp1Ob9H7JEmSpN4T++8j2Pk7f+XfTHqPtULW9OiLkAcCPhK2sSBdTGv8vW+tirNZZo/oD2HIqasWcmPNMNaGkCHf2X3t75KxCAwr7XE3kiRJkqTV0pv0YKd64qwU25njjz+es846i0WLFnH55Zfz3HPPcfnll29waM3NMXbsWH77298ybNgwrrnmGu666y7OOussdtllly3a74wZM3jhhRf45S9/SVtbG5dffjnHHHNM5/LLLruMAw88kL/+9a/84he/4JlnnuELX/gCP/7xjwGYNWtWl/0dfvjhRCIRdtttt42GokOHDuXGG29kypQp3Hnnnfzyl7+kqamJCy+8kK9//eubXcPAgQO5/vrr2WOPPbjpppv44x//yJFHHslnPvOZLXqPJEmS1Lsip+1H7geHbmStoPO/eSIUkSKVC5lYW0cst3YO8LJUmmGNzZ3Dh1a3tTO+vp6q5jbK2tr50ZmV3H14isGFOYYW5vjHESm+uWe+h+NJkiRJkrT1BWG4gQkvpNXOPPNMlixZwj333LNZ21100UX87W9/Y/bs2Vu1ntraWj75yU9y/vnnc9xxx23Vfe9IVqxo6u0SpA9cEEBNTSm1tU0bmr5J0nvwGpK6Ss9bybsf/TttFLEpQ4tWsIqbTz6YuWGS3VfWs6C0mEQux7DmFqIhLE8WQJinf6pjzvA88PGvD2fCPmXvvWOpj/BzSNoyXkPSlvEa0rbSr5/DgnxQgq/VbZX9hP9TtVX2sz1xWFFtd+68807i8Tif+MQnersUSZIk7UDa67KU0kYbxZuwdkhISFNxAfMiJQxtbmFcfWOXNfq3p8gEAY3RKGW5HHkwGJQkSZIk9TrDQW2yXC7HqlWrNmndkpKSrX78GTNmMGfOHGbOnMnxxx/fZR5CSZIkaUtFqxK0kux8XUI9LZQREiFOOxkKWDPnYA31ZKpa6D+ogIOfW8rywgKGtLZ32V8IxMOQxwfUsMfKVaR9JF2SJEmS9CFgOKhNtmzZMo4++uhNWveHP/zhVj9+a2srs2bN4sADD+SrX/3qVt+/JEmS+rZIHMppoZkSCmglJMIAFjKCtymlibeYQEgZSVIsp4J/XzaEC08Ywg9PqGXnVY3k6Tqp+1MDaujf1k57LMpTg/pz8PxFvXVqkiRJkrTj2fhsENoAw0Ft1LXXXgtAKpXi6quv3qRtxowZw1FHHcVFF1201eo4//zzOf/887fa/iRJkqR1RUOoookl9CNFESmghTIaqWAyjzCcd2lgDACVNECsjKKyOIP2LWPRs43E83nerigjmcvzbmkxS4uLmJvLccJb77KgtJho3p6DkiRJkqTeZzioTVZQUMC+++7b22VIkiRJH4ggEqGBQvJd+v9BK2U8xjQm8G8idAwXGpDvXH7y2f14bVIhf7x+FS/0q+6ybSYapaEgwejGZuqjXfcrSZIkSVJvMByUJEmSJIAwoJaKHhdlSfA6uzOBeUQIiZDpsrxqZCHVbUsJwpAwCBjY0srE2jqKM1mKM1kAIhHHvJEkSZIk9T7DQUmSJEkCUu+2MIA6MjSTI8IS+nVZniVGanU0WFDS1GVZMhdSms0yaflK3qwo5RMLFhNdbxTRTCT6QZ+CJEmSJEkb5bg2kiRJkgS0zKqllBRVNFFGaw9rhMSpo5x5RG77j66LVud+u9bVc+Q7C7sFgwDLkgVbvWZJkiRJkjaX4aAkSZIkAUFJnDWZXgEZ+rFqnaUhASH1VJH6y3nkJw7vsm1949o0MBF2TwbzQLtzDkqSJEnS1hMEW+erD3JYUUmSJEkCSqdUdXk9iJVU0kgbBRTTzipKyBZXEp0yimw222Xdgvh77/ud0mI+UtcADNvKVUuSJEmStHl8dFWSJEmSgGh5nBWUd2lLkqGSZhJkKSJFpryox20ryjd8axUCy4sKqU9sJEGUJEmSJGkbMByUJEmSJCDev4wUa4cWXV+aGO1hzwFfY32W/Aa2C4DdV9bzzKD+W6NMSZIkSZK2iMOKSpIkSRIQxKIMoo41M07UU8JyKskRoYRWSmilqKG5x20j0QhZIEpHT8H1Z60ozGZpjXn7JUmSJEnqfd6dSpIkSdJqkdX9BptJMp8BrIn5VlFOhhj9Io09brdiZbbz5qqn6eyXFyapSaWA0q1esyRJkiRJm8NhRSVJkiRptSYKAainlPVjvmaKKK9s7XG7qopoj6HgGpVt7QxuaNpKVUqSJEmS9P4ZDkqSJEnSagsYQBsxIuS6LQsISaeiPW6XT+Xe8+aqAKhJZ7ZOkZIkSZIkbQHDQUmSJElarZqlDOdFBvEWAfkuyypopLU50eN2K9vXDEi6YUW57oGjJEmSJEnbmnMOSpIkSdJqI3mbAFjJYMYxnzrKyRGhgmYSpFlZUEN5D9uN3znJzCAgFm44IsxG3mvgUUmSJEmStg3DQUmSJElaLUEKgIAoBaQYxMouy5PJnnv/JRNRlhQlGdbStuGdb6xroSRJkiRJ24DDikqSJEnSam0jRgBQREO3ZRmirKqPb3Dbjx9cTF0iTnMsRjbo3kswF/H2S5IkSZK2miDYOl99kHenkiRJkrRa7slvs3LcLvRjPu3r3C7lgcXUkA+iG9z2kJMH8tWfj6J4SILGePdBWlIxb78kSZIkSb3Pu1NJkiRJWiMeI//E+fyb/XiT0bzNEObTn9cZSQMllI/ecDgIUFKT4KzLRvNuv1IaY2sDwqWFSb51+fAPunpJkiRJkjbKcFCSJEmS1lNGC0naaaWQesrIEqUfdcR2qtik7X/zs8EMOGoAbw2qYOX4Ks79n5GUVSY+2KIlSZIkSdoE3ce6kSRJkqQ+Lko741hIO3GyRCgiRStREt+Zssn7+Nz0Sphe+QFWKUmSJEnS5rPnoCRJkiStp+jZM0mTJkqeJBmyZGipKqFgdGlvlyZJkiRJAgi20lcfZDgoSZIkSetJjCyFl75B7cRR1FWW0Xj+UQx8/azeLkuSJEmSpC3msKKSJEmS1IOCgUUMvu+zvV2GJEmSJElblT0HJUmSJEmSJEmSpD7CcFCSJEmSJEmSJEnqIwwHJUmSJEmSJEmSpD7COQclSZIkSZIkSZK0nQl6u4Dtlj0HJUmSJEmSJEmSpD7CcFCSJEmSJEmSJEnqIwwHJUmSJEmSJEmSpD7CcFCSJEmSJEmSJEnqI2K9XYAkSZIkSZIkSZK0WYLeLmD7Zc9BSZIkSZIkSZIkqY8wHJQkSZIkSZIkSZL6CMNBSZIkSZIkSZIkqY8wHJQkSZIkSZIkSZL6CMNBSZIkSZIkSZIkqY+I9XYBkiRJkiRJkiRJ0mYJeruA7Zc9ByVJkiRJkiRJkqQ+wnBQkiRJkiRJkiRJ6iMMByVJkiRJkiRJkqQ+wjkHJUmSJGkreH1elv+6Nc2ibJTB8Rw/O6WAEYOjvV2WJEmSJEld2HNQkiRJkrZQU2vISX/M80i8mLeSBTwaK+aTfwhpb8/x5twM8xZme7tESZIkSdrBBFvpq++x56AkSZIkbcSKC56k7uHFVFTHqbz8ACJjS7ss/9PDKRYlkxCGHQ1hyIp4jFN+VE8hAYW5PNXxLD/8QTWFSZ/RlCRJkiT1Hu9KJUmSJOk9PPGJe3n9waVkW5t5ZWXA88c9RGZBc5d1Xnmmtsdt55QUc9/AKv4xqIqXY8Vc86sV26JkSZIkSZI2yJ6DkiRJkrQBdS+tZOy7rzOsaQF5cjw9ZDRvDxhB5TefgS+uXW/IguVEygbz3afu56RXZ9NQUMjlkw9l1vAJEEI6iDC7soTBC1p672QkSZIkScJwUJIkSZJ6FLZlefIbz/GxdD2RaBOHTz+f2YNGEwL9mlr5r18/QeaYFADR4jg/fuz/+O7T93duf/td13P4yedRnB9CczTKkqIC5pYW99LZSJIkSZLUwXBQkiRJknrw96++QNWyelaWDuSZ6o9yzMJGPrnsVbJBwMvlZdw1YDw7D/0TH5l7DEvGDeSMe/+3y/bRMOQbz83k2ilHAdDS3MqLxcneOBVJkiRJkjoZDkqSJElSD4peWki2oojH+o0lE4uSi8eIAIkwZO/6BgbMqSXWlmHejFeZtWIEzbFEt30sLi1nQUmSwmyOmvYMw9ozQPf1JEmSJEmbKejtArZfkd4uQJIkSZI+jAamGpnbbzDQ841TUBaQJeDZW2opjUS4fvePdVl+y2778OVPHcsLNWU8NbCSZ/qXU5XJboPKJUmSJEnaMHsOSpIkSVIPUok40VyOj732Gq+OGMb8/v27LK/KNDCOd5hdOIgxq5qYNXZ3fk6Mye/+m+ZEkrOP+Rz5yNpHWZcXFbCy0F6DkiRJkqTeZTgoSZIkST1ojwaMW7yYgfX11BcVsaCmH+GasC8MSeQyVFNPZSRDJsyTIuCVwWN4ZfAYMkFASyLebZ+N8eg2PgtJkiRJkroyHJQkSZKkHuyy8g2WxkaRjUR4u19/KlbUEcnlyUcj5KMRxr+zhGbKqG5poaqllXuGD6Iwn2dEKkNhLsfBC5azorCA1ytLyUQ7BiYN0rlePitJkiRJUl/nnIOSJEmS1IMl8QE0BwU0FBYS5kNi2RyRsOO/8VSGuqISMiQZt2wJfxk+mFQu5MmyEl5JJihJZ9i1romDFtVy/NuLKMjm2KWuiYHpTG+fliRJkiTtIIKt9NX32HNQkiRJktaTr09RFyknly+gJZcmks13uWcMgLZYx7ChVa0tVDa3sDyZhAD2aWzusq+a9jTHzl1KezxGY6Rv3nhKkiRJkj48DAclSZIkaT2Lzn2CivqAgkwL+SBCJMyTD7oOvFKcSgGQB5oTCQggUl1EYn6eZ6rKmFdcSFkmy0frGonn87QDhOE2PxdJkiRJktblsKKSJEmStJ6VsxooyHTMDxgJoawpzfLKMlqSBQAUp9oZ1FQPwL+GDydWUECkIEo+EeXOEQN4Zkg1SyuLebOqlDuG9WfsOwt4u6qYeRVFvXVKkiRJkiQB9hyUJEmS1AdE7nqJ+Ll3EabyBBUFpO44FXYfvMH1W+IJymkD4LVRg/jH/ruTi3Y8W1nR2Mze897l+dGjeXvAAJ4dO5aPtrYzNpPhwbIkVc1t/NftTzBm6SpeGtmfKw7fm7ZMmvnFCQrzIbc8nKKmLODQPeNEHGZUkiRJkrSNGQ5KkiRJ2rEtaSBx1m3ESREhJF/fRnDor2hf9mMIeg7nlteUMXhpA9lIhAf23bkzGASoLyvh0Z13JhsJumxfmckxuL6Fy296iLK2NACT31zMlSsaueXwvahIZVlRnORr8zt6D455opm/fyGkssLbMkmSJEnStuOwopIkSZJ2aMnP3UCCdiKEhECEfMfrW2ZvcJvlVUX88YCJLK4uoz2Z6LY83ECHv33eWNQZDK4xcFUzyUzI2Lo2PjvrVa699m5+/od/UFrfxNevatySU5MkSZKkvivYSl99kI+oSpIkSdqhRV5dCkC4+s6vIyIMid86i9QpH+1xm/KGNl4aN5C5mf4kMlnS8XVuncKQovYUmWiU9oIExak0pakUuSDC4HcWddtXPoC2ZJyP/fstjn3qX53tB73yDt849citeaqSJEmSJG2U4aAkSZKkHVqQz9AxaMqaR0I7/hs2tPS4fvNbjQS5PPvNX0QA7LR4KW8OGUgmFiOay/HJ5//Fx195leVFZdx6wFQKImsHZGka2o95SxoYsXxtj8DnJwynuSjJ1NfmdDlOIpfniH+9AfTbeicrSZIkSdJGGA5KkiRJ2qFFgjwQXa81IBoLu62bT+d55jNPkB1Z1Rkllre2sdu7CxlQv4qpb75Bcbpj2NCBrY1UpNO0JZOd28dDuP2QcWSbo+y6eCWtFaW8Nmrg6qXdjxfJd2+TJEmSJOmDZDgoSZIkaYcWht2nkQiBbLygS1v63yt45ciHWTV+IKlohDeqK4gQUElAGIux/1trg8E1stH1Q0d4q6qaV8fV8H97jaUylWFQa5qyVAt37TyGLz/5Qud6mWiEWyd/hC9spfOUJEmSJGlTGA5KkiRJ2qFF8qnVMw0m1raRJh7LkVlnvaVH3sV+ra/wQnw6l35sb5oKOtbv15Zi+tzF1BcXd9v3gPpV/GvYMJYVFZLI5xnS3MzS0rXrrSqIs6ogzvgVDcyeMIbfFSXY5Z1FNCcLuOmAPYiS+8DOW5IkSZJ2aOs/BapNZjgoSZIkaccWBMTDVeQpICRGhAwBaVoLJ3Su0vZ2A0PaFrOMCdw3clhnMAiworCA52vKqdlpJ8YsWUJZWxsA7fE4/xo0kOcG1HSu+3K/ClZVlEAQQC6EVI4gn+fjCxeyx/L53LzXJH5x+D4dZYUhH50znwXz2xg2vHAbvRmSJEmSpL7OcFCSJEnSji0ICUIIyJInQkCGAAjqWjpXyaRyZMMaErQxr6Z7ULeyIEFzMsn/7jOZgbV1xMIcy/rX8Gr/tcFgJhrw5uBywsjqx1cjAYR5bvzTDXzu388CcPbT9/P1o0/kyo99gjAIiKUguqIVDAclSZIkSdtIpLcLkCRJkqQPUj6I00ol9YymieGsYiztlBNk184fmIhGyBOjP2/z8XmvddvH0NY2+i+rpd+Kuo4OgUQpqm8mFwSMW7iCj8xfRmMyTj6y3rg28ShHvv1yl6YL77+HWC4LQP+6VgaML9v6Jy1JkiRJ0gbYc1CSJEnSDi2dLyRHKWsnpIjQSj+ikWjnOk2pPGEsIJlt4ScP38brNYN5Yvh4gjDPAQveYfe6HKWNzQDkIgELBvanrSDBt+94jIGrmgAYuLKenx07pcuxk5ksbyR3IqxqZtLSd4nn81S1tVCUTtMeCzji2bcJs4O3xdsgSZIkSRJgOChJkiRpO5V/fSXh7MVEDhtNMKC4x3Wij7xJLozRfab6KJF12pafdj/9I820UUi/1iYe//2PmVvRj8Jshr/t+kneqh5DJMxTnGln9vidWF5dxeR/vd0ZDAJ8avZb3HDIHqysWFvLuIXL+djZZ5GLRBjUVM9dd/yG5mSS4hR89uk3WFFezC2nP8IXrt6XcED5Vnx3JEmSJEnqmeHgduyoo45i0KBBXHvttZx55pksWbKEe+65Z7P2kclkWLVqFf379/+AqpQkSZK2TH5pM/klLUR360cQ65gZIXPYzURfXEIECIHsKbsTv+LwLtuF+RDOvpMCmkhRSNeAMAepkPzSJjL/cSc7LZ1PabaWt9iVXXiOKHlG16/g5UETeHXQeEauWMjxbz9EaaaNwxc9zY37HkHNqpYuxyvI5vjRTQ/xXyd+jIPmL2DQygau2uejRPMhu61qpCaV5VvTvsDc6mImrGhi8pI5zNx9Agsry5l05J8Y/KtPUfHRIR/oeylJkiRJO471HwLVpjIc3EF88YtfpL29fbO2WbJkCV/5ylc47bTTOOqooz6gyiRJkqT3J8zkaNrtRsK6FBASxAKK7vkskT/NIvbiYtbcCAZA9I8vkfzbk4T7jyY3sob22SspeeYNYqQpoIkIEdqoWb12niQraZtbyBuH3MW8qiEcEpnDm0NGMj8cQd2yKvrnlnPXwfsyt/9IgnyOzz/7d0oyHb9vV7S18OUn7uZ3O3+aUQtru9Q8d2g1zZUlTH54AcsTSQA+vWApI1vaOtcpisapLSlm70VzmTT/LX52+Kf5094H85+n3Uz5y+cTBN7gSpIkSZI+OIaDO4jJkydv9jaLFi1i/vz5H0A1kiRJ0ubJv9NA2JImN7+R/EsriU4eSOvZ90NdGujoLRhmQ2JHzKCKxQRAilIyFFJEHQE5sg1FZP8vRYxW2hhIE6WU0EiMBM2UkSJOAa0UUU8TA8nlY4xZUUtZS4qZI/ejubCQZRUVJEZm2OWVGAXNMCa1hMH1SylJd30QryCboa0K3hla0xkQvjyiP9cdvhepeIzvHXs4v7j5Hga1tncJBgF2qW3kj4OrOO8zJxIhQkEmw+D6JYyqW8rvD/tfDmicx21nHc/HjxrGHv0DIkvqCBpayY0fAgaHkiRJkqQtZDgoSZIkqdeEzWlSX/wn2UcWEHa2BmRW/7frMDEBbVQSZQH51YFhlDQBOaJkidJIhoD57EJ/llPGyo5jUEyeAgJihCTJUkw5i8iRoJ7+VLS1M/nNtwBYWlHBRUcexoWH7ks6GmXPJbWc/GKKPAGRdSrMA03xIp7bpYpXh5Vz8/57MK9/RefyTCzKT484gPKWdLdzjgATljV2nkMqHmf2kLE0FUb4ykv/R0Cej3zvdS554Biu+eju3PbT/yLIh2QnDKHhD18nP6Lflr/xkiRJkqQ+K9LbBWjT3HfffZx88slMnTqV6dOn8+ijj3ZZfuaZZ3YZGjSdTvPzn/+cY445hv32249PfepT/Pd//zeNjY0A3HPPPZx99tkAXHzxxUyaNKlz29dff51vf/vbfOITn2DfffflsMMO44ILLmDZsmWd61xzzTVMmTKF+fPn87WvfY0DDjiAgw46iB/+8IfU19d3qa25uZmf//znfOpTn2Lq1KmccMIJ/OUvf+myTmNjIz/96U854ogj2G+//TjuuOP405/+RBiGvB8PPvggn//85znggAM48MAD+c///E9eeOGFzuWzZ89m0qRJ3eZoXL99zetnnnmGH//4xxx00EEceOCBXHzxxbS1tfHEE090fl9OPvlkZs2a9b7qlSRJ6qsyM/7VLRhco6ffBLMkyBFjFWNoZjCtDKCOsbRRQ44CVjCcJO2UUd9lj5WspJhmEmQJSRIhTzG1RAiIrvM758D6egraW2hNxMlGI8wa2p+7dhnHrCE7d6njlarRDFuwknHzFlGYy1GXTNJveTOVda1Eszmi+TwLyst5u6qUtmjX2666ZIJ82LUHYD4S4fnBOxGQBwLipPnBg3fzRqqAtyoHABB7fRElP7h5895gSZIkSZLWY8/B7cA999zDxRdfzO67784555zDggUL+N73vkcQBAwaNKjHbX7605/yz3/+k5NOOokhQ4YwZ84cbrvtNubPn8/VV1/NnnvuyWmnncYNN9zAZz7zGfbcc08A3n77bb70pS8xfPhwTj31VJLJJC+++CJ///vfWbBgATfddFPnMXK5HGeffTYTJ07kvPPO49VXX+Xuu+8mlUpx2WWXAZDJZDjzzDOZM2cOn/nMZ9hpp52YOXMml1xyCe3t7Zx44om0tbVxxhlnsGzZMo4//ngGDBjArFmz+PnPf878+fP5zne+s1nv13PPPcf3v/99pkyZwjHHHENbWxu33347X/nKV7j11lsZOnToZn8PLr74YkaNGsVXv/pVnnvuOe655x6WLVvGG2+8wQknnEBpaSk33ngj3/nOd7j77rspLS3d7GN8EBx1Sju6NT/j/qxL74/XkD4M8s8uWedV1x/GgLBbWwHNtFFFnvg6rRHaqSBKhEJS5HuIFQMgQn51b72AFBUkaCZLQbd1d1q+ssvrV/pVMnP4Htyy60RSBTkaYiVMfWsFkVzI1ZN3Z15VBZG2HIWZHKXtWYpaMxzx9mtct89HycZj/O+wgXx82Ur6pTMsKivkkREDGL+siVi+a51DG1esU21InDRHv/oc946fyLgn/wlA/Ok3vWa1w/BzSNoyXkPSlvEaktSXGQ5+yOVyOa666ip23nlnrr32WmKxjm/ZhAkTuPjiize43T/+8Q+OPvpovvKVr3S2FRUV8dRTT9Ha2srQoUPZd999ueGGG9h999355Cc/CcDtt99OEAT85je/oby8HIDPfvazZDIZ7rvvPhoaGjrbc7kchx12GF//+tcBOPbYY1mxYgUPP/ww7e3tJJNJ7r77bt58800uueQSpk2b1rm/M888kxtuuIHp06dz0003MX/+fP7whz8wduxYAI477jiuvvrqzvBy3Lhxm/ye3X///SSTSX7xi18QrP50nzx5Mueffz6vv/76+woHa2pquOqqq4hEInzmM5/hueee49lnn+WXv/wlU6ZMAaCwsJBLLrmEV1555X3NAbm1VVYWEYtFe7sMaZuorv5wBPLS9sprSL1p5V4DaXxi0epXXcPAgDz5LkOLhlSzkBQV3fazZojOchpZyuBusWJIQJ7oOq871i+giRRlXfb15OjhXV6Xp9L88aPjeXlY/862O/fMQEOacJ1ege2ROAPbMyTyIY3R5OqTCFhWVsStRQUQDaAwDrGA6sbFNBQP7Pxr1MfnPM/EJW+v8z5AjigLy6v40jMPdh4jMnEUNTVes9qx+DkkbRmvIWnLeA1J2zHD/ffNcPBD7vXXX6euro4zzzyzMxgE+OQnP8kVV1yxwe0GDBjA/fffz84778zHP/5xSktL+fKXv8yXv/zl9zzed7/7Xc4+++zOABA6hgUtKOh4orqtra3LssMOO6zL9uPGjePJJ5+kvr6egQMH8vjjj1NZWcnhhx/euU4QBPzoRz8im80SBAEPPfQQY8aMoaampsuQpAceeCA33HADjz/++GaFg/3796elpYXLL7+c448/nlGjRjF27FjuvPPOTd7H+g488EAikY4//EQiEYYOHUpzc3NnMAgwePBgAGpra9/3cbamVataffJJO7wg6PglfuXKJt7nKMRSn+Y1pA+D/Jm7EfnHXPJvrVodiXXEeiEhARAlx5o7voCQZgYSXT0j4bpiq9uiZImQpZ5KymggujpgTFHMuneOCZoAqGQBy5lAnih5AvIFjczpv/b33Vguz24Ll3H3Ryd0OV4YjUJkvV+2goCWWIREJsdLw4atbY9GIB5AQRSCgJKGFmZWDSMeBpSEeTJhyOGvPb7u3skT8E5lNYmKJKPqO36/zFeV0PiD48nWNm36Gyx9iPk5JG0ZryFpy3gNaVvx4T59GBkOfsgtWdIxzNL6vd2i0SjD1v2Dw3q++93v8r3vfY+LL76YSy65hN13352Pf/zjHHPMMZSUlGxwuyAIaGho4IYbbuDtt99m4cKFLFmypHPuv3w+32X9ioqKLq/j8XiX9ZYsWcLQoUM7e/Ctse5wqAsXLiSVSnHooYf2WNPSpUs3WG9Ppk+fztNPP81tt93GbbfdxpAhQ9h///055phjNitkXFd1dXWX19FolMrKyi5ta8LD9ztP4gfhQ1SK9IEKQ3/epS3hNaTeFPQvpvDRE8k9vpD8kmbCbEi4rJXoxP60n/FPaM12rLc6OiylnhwRAnJkKGHN/HxJWgDIk6Yf7xAlIEJIhBwxcqv/V0xIhBgtxGgiQxGtVFNIK/OK+/H8kFEMb1nAf/39cR7aaSciZDhj9r186uT/7F74Bi6aAIiEIYNTGV5fp71fS5poJmBpaSHJ5hSZIEo6gLrVvRmv2/0gBrS38Vq/sUxd8BLPH/xRyn9yLJcOSLDqyAuIrGomfcAuUFzQ82SM0nbMzyFpy3gNSVvGa0hSX2Q4uJ1ob2/v1vZeIdQ+++zD3/72Nx577DGeeOIJnn76aa644gpuueUWbr755m7B1hr3338/P/jBD6ipqeGjH/0oU6ZM4SMf+QhPP/00N9xwQ7f11wRiG5LL5boFg+vL5/NMnDiRM844o8fl/fr1e8/t11dSUsK1117Lyy+/zCOPPMKTTz7Jrbfeyu23386PfvSjzuFNN1RLT6LR7sNzbuy8JEmStGmCWITYQcO7tUceOYHWKX+CbL5zGNC6686g8Of3En1tEUW8Q5SQPIXkSBAkc6SP3Y/chCGkF7RSdv1D5HM5ssQpZylQ17nvNipoo4qQKAvKq3ircDgDFrcwZ/goCAMOfnM+AHNLduY//vUU1x90KMTW/u47sLWB5nRAc0Fy7XmEIYNbWxmSCcnHIkQjISPrWxnR1EZxJkeEPI+M6k9bLEEsnetyrqlojFcGjmT/u6eTLzuViessy05+fw+4SZIkSZLUE8PBD7k1PQYXLFjQpT0MQxYvXszo0aO7bZNOp3nzzTfp378/hx9+OIcffjj5fJ4//vGPXHnlldx7772ceOKJPR5vxowZDBs2jD/84Q8UFhZ2tv/zn/98X/UPHDiQt99+u1v7zJkzue+++zj33HMZNGgQra2t7Lvvvl3WaWxs5Nlnn2X48O5/KHov8+bNo7m5md12243ddtuNc845h7lz53LGGWdw8803M23atM6wL51Od9l25cqVm3mGkiRJ+qBER1ZQ8s6ZZP9vDmFzhthJE4jEo6SPGk9m3NXkGwYTo52QKMHkwcT+ekrnthGg+ZIjaNn/91S++Vq3fRdST3ZQGbnvHMaEH9zJiuU1ZIojrD8uey4S44cz7ycXj3HrpMlkC+LsuWgev79tBmEuz8Gn/JClhYUQQBgJeL2qgpKVK1leXcagVJpxDa0AZKMRXhxYQUNhgngeYnVtXY5z6Dsvs+9tJxCWFW39N1KSJEmSpHW8d7cv9brx48czePBg7rjjji69B++9994u8/Otq6GhgdNOO61LT79IJMLOO+8MrO0Ft+a/6/aWq6+vZ9CgQV2CwaVLl/LQQw8BHT0BN8fUqVNZuXIlDz/8cJf2W265hSeeeIKKigoOPPBA3nzzTZ544oku61x//fV897vfZc6cOZt1zJ/97Gd84xvfoLW1tbNt5MiRlJaWdvZ0XDNM6Jtvvtll2/vuu2+zjiVJkqQPVlAQJf7ZcSQ+vwuReMfvr0EQEH/zKySuPITgpInE/nZyl2BwXckZR9BE2eoZDNfKEyFdVErm5Ck0zf0ZFAaMTHd/qC0I87QHSX73f3+g5eKvkPr+mTx99UVMWLGYj9Qt5bi5r0IsCtEorP5d84XKSuZXFDK2tomCbLZzXyuLOubxzhQnaK0qJJuIEo2EnPjKixz030cR6+dcJJIkSZK0yYJg63z1QfYc/JALgoBvf/vbfOtb3+K0007j6KOPZvny5dx2222Ul5f3uE2/fv2YNm1aZ6C4++6709DQwG233UZ1dTWHHXYYQOfQov/4xz8Iw5AjjzySKVOmcP/99/OTn/yEnXfemUWLFvGXv/ylM5hsaWnZrPo/+9nP8te//pXvf//7HH/88YwYMYInnniCZ555hv/6r/8iGo1y6qmn8tBDD/Htb3+bY489ltGjR/PCCy/w97//nSlTpjBlypTNOuYpp5zCueeey+mnn86RRx5JIpHg0UcfZeHChVx88cUADB8+nI985CPcddddFBYWMnz4cB555BEWLly4WceSJElS7wiCgOCk3eCk3d5zvejE/pRHFpHPxwnIEiFPnigBeRJBijWP343+9d5Uff4JxtW/zZsVYzu3H13/Dk9X7sncxAj2rX+O8nw90PHAXC6I8I+R47sdM0/A0a8tZKdVzQC0RaMsKCuhJJ0ltTrgzBQnyBQnyKfSnH/d3pQW+9ymJEmSJGnbMBzcDnzsYx/jf/7nf7jmmmuYMWMG/fv358ILL+T222/f4DYXXHABQ4cO5d577+W+++4jmUyyzz778J//+Z9UVFQAHb3pTjjhBP72t7/x6quvMmnSJL73ve9RVFTEo48+yv/93/8xYMAAPvWpT3HQQQfxpS99idmzZzNhwoRNrj2ZTHLNNdfw61//mnvvvZfm5mZGjRrFZZddxqGHHgpAeXk5v/vd7/jNb37DAw88QGNjIwMHDuT000/n1FNP3ei8huubPHkyv/jFL7jhhhu47rrrSKVSjBkzhksvvZTDDz+8c73//u//5oorruDOO+8kGo1ywAEH8I1vfIPjjjtus44nSZKkD7c0xSRpJaSAHBECsoSkiBSufUK0bNooiAUctOhxdqqfQ22yin5tK3kxOYmSIEdrUM7DFQexd9MzDM8soCVewNcOPpklhcluxxve0toZDAIU5nL0b21j/PJGZg+rIh3rCAiTmSxFqTQlRQaDkiRJkqRtJwjDMOztIiRtfStWNPV2CdIHLgigpqaU2tom/DSTNp/XkPqK/MCLqc7XdRlYNEWc9IQBZB77dmdbcuA3KcnXEZBnZbKSJ/rvR128mqLWDIlUlhXVZSwaVEVBtp2FFeUsKEjw/OB+LC7vOhzoPotr+Vh9I6/0K+fFgRUATFxcR1UqT1s04KUBFcRyOd6uKoUgZPHpmW3wLkgfPn4OSVvGa0jaMl5D2lb6OX3ABya4YPNGOtyQ8NLirbKf7Yk9ByVJkiTt0EojTQT5rm0JsqTTXUO5TCROPl/KisIy7h5zMLlIx+1SW1GcdFjAv3cb2bluNJvj0Dfn8sk33uGPEycwc+SQzmWT3lnGazsN4J/jBne2LSkrYviKJpYUF5KJru4pGAKxvjm/hSRJkiSp9xgOaruwatUqcrncRtdLJpOUlJRsg4okSZK0vQiJAev3zgtJ50u69CZsHzmS6Px3+Hf1uM5gEIAgYM6YQV22zsWi1FZXMmTpCj7zyts8NXwQEeAjja0EpYUUpXOcOWsOzYkYzw6t4u3qUuZXFMN6IWW315IkSZIkfcAMB7Vd+PznP8+SJUs2ut6RRx7JRRdd9MEXJEmSpO1GPsiRJ0JknSSulVKC9XoOxu49jQem3c2K4v7dQrtctPu8gLnVc2MXZ7Kc+M5iiMeJhyHp4gJq2jv2nczm+ejCVQxsSvF6dQm1iUSXfUSzjmElSZIkSdq2DAe1Xfjxj39MKpXa6Hr9+vXbBtVIkiRpe5KgnTbKKaCdgDw54mRIEEu0k11nvaC0gH3/9mlu+fxsStqyXfZRVd/E0v5VaxvCkKr6BgDaChLEYzEIQwhDIqsnrUlFIzQVdISBA1tS9G9J8cjQauqSawPCylT6gzlpSZIkSZI2wHBQ24WJEyf2dgmSJEnaTuWIEBAlzdpJ5hNkSFHQbd2CygSfv2s/7t/3H+SSCfJBQLIxQ1NZO+mmZpoKk0TzeSqbW0hmsmRjURoqS3s8blus6+1WBBhT30LdwNXhYBRaC2JAttu2kiRJkiR9UAwHJUmSJO3Qgmi0+5SDRCgIMrT3sH40FhCJZhk3t55YLk9LQYIF0YFUNzVT3dTcuV5LWTFBEFCUzRHJZMjH4xAEZCIREvmeJxOMZXOQWD3TYRCQy298Xm1JkiRJkram7hNnSJIkSdIOJLdTf6Dr3H4BOfKHTdjgNpXZBl4bPYD6kiSxfI5k23pD3IchBEHnyxHLa3msrJiX+pfxz50GcudHhrAyHu26CbAgWdCx3eptRybsNShJkiRJ2rYMByVJkiTt0Np+dyYJVgIdvfkCciSoI33RZze4zZDGJRBmqY6+y8TcI0z/9x2Ut9UTAvnVwV6wzvqLigp5p7KYfw2uZHFZIctLkjw6pJpXkgWsikZYGY3yYmGS2vJk5zaJIM89J3wAJyxJkiRJfUGwlb76IMNBSZIkSTu2EVW0fPszJFhJkqXEIvU0/uYsiG34dmhlQSW7rHidiQ0vU5RvZ1Drcs5+9gZ2X/oSTYVJMuvMJ9gQj3HP0EGQX6d3YhiSLojx2qAKHhlazVuFMKSlkX0X1XPpR9Ncc0CK+Z9LUZHs4eCSJEmSJH2AnHNQkiRJ0g4v9+1DafzGwbCyFfoVdxkStCcrioawS8PD3donrHibZ0Z8lOLmdl5MJnl87FDmFReRi0SgLQuZHMSjkAk7OioGAblolIX9+3H0uw/z0sBdOWNn5xmUJEmSJPUeew5KkiRJ6huiEehfstFgEKD/Af1JRQq6tTcnigkjEVZVlXHA8jq+/MgLTKxrIEYIkQBqW6GxnXi2+1yCL/UfQrRbqyRJkiRJ25bhoCRJkiStZ9efT+LdghFk14nz0tE4M0ft2/l62YBKps5byjHLVvDZJcsYkc5QVhSD0gIyRQlIRrrccTUVVtIe8RZMkiRJktS7HFZUkiRJknowoekNFhcMJh6meaf/cJ4dtTd1xVWdyxOpDK98ZBjZWJSybI5oYYzG8sK1OwgCiEcglac0lWJQe8irhfFeOBNJkiRJktYyHJQkSZKkHhQE7fRP1QKwNNufVYUVaxeGIZlIwIKhNaxMxHl0YD/mlhR230kkYOfaBiraM7ydLKAxas9BSZIkSdo6Nj5lhHrmnakkSZIk9eDu0QfREC8BYM+l/+YrT1xH/7plFDU1U7O0lrGvL2L4iwv567DBFIdAGHbfSRhSnsoRCSL0z+ZI5vLb9iQkSZIkSVqPPQclSZIkqQeLq4dwR/wYxtbPJZlLMbdsBNG2kPLmRgKgOJumpD7D8KUrqB3YD3IhtGehINoxpGgYQhCQjwRE8yERYFA229unJUmSJEnq4wwHJUmSJKkHC2qqSKRzvFo9YW3j6t6BYRhS0p4mRZwj3lzED0YPgVS24ysSQDSAfAgVSWL5tT0Ki+w5KEmSJEnqZQ4rKkmSJEk9KM62MnP8GFoSCUoa26msbaG0vp22SJSRS1YBUJDPsNdBpVRn1ukRmA8hk+/oSZgPyUTXzoNRnrHnoCRJkiSpdxkOSpIkSVIPPrlHlpWlJRS0ZEi2Z4nmQwoyOYauaKJ/fTPRdJ7CSJqJXxtLeaLn0C+WyxPP5iEMieVD7DcoSZIkSVtJsJW++iDDQUmSJEnqwS7fnMTOC99h4KqmLu0B8MqgGsJ+OT7yzPEEhTGKUu1E1h8yNBFl3MoWCkNIhh1zOjTF49usfkmSJEmSeuKcg5IkSZK0AdPGxWh7rvtTldFTduLNnduYOmkATUtX8ZHWWvrlYywriLMyESNJyPBVzRQABGsfRQ0jffSxVEmSJEnSh4Y9ByVJkiRpA/b+6liWVBR3aastLeSgr43q0pYsLKEIGJXKMKmpjV2b2ikLw24j1IyqDj/YgiVJkiRJ2gjDQUmSJEnagERZnE9eM5H2MeWsqCwitXMVn7prP4L1egCO3q26x+2LWtvJE5InpCQJl/1ncY/rSZIkSZK0rTisqCRJkiS9h8qdK5j2l/27tGWz2S6vTzm0gCeeSZMM1/YMDLM5rvl5DQtX5EnEAwZWR7dJvZIkSZIkvRd7DkqSJEnSFqooDvjemcUUlEdIR6CiJM/Pv19GJBph+MCYwaAkSZIkbW3BVvrqg+w5KEmSJElbwe4jo/zmu6W9XYYkSZIkSe/JnoOSJEmSJEmSJElSH2E4KEmSJEmSJEmSJPURhoOSJEmSJEmSJElSH+Gcg5IkSZIkSZIkSdrOBL1dwHbLnoOSJEmSJEmSJElSH2E4KEmSJEmSJEmSJPURhoOSJEmSJEmSJElSH2E4KEmSJEmSJEmSJPURhoOSJEmSJEmSJElSHxHr7QIkSZIkSZIkSZKkzRL0dgHbL3sOSpIkSZIkSZIkSX2E4aAkSZIkSZIkSZLURxgOSpIkSZIkSZIkSX2E4aAkSZIkSZIkSZLURxgOSpIkSZIkSZIkSX2E4aAkSZIkSZIkSZLURxgOSpIkSZIkSZIkSX2E4aAkSZIkSZIkSZLUR8R6uwBJkiRJ2l48/0IbT/9hCTVjC0kOCXq7HEmSJEmSNpvhoCRJkiRtghs+/xIVc1YyrrWd5qcKeGPkzrSdlOvtsiRJkiRJ2iwOKypJkiRJG7H8rWaGvryIktZ2AEraUuzx+rv8/IRZvVyZJEmSJPVRwVb66oPsOShJkiRJG/HycQ/RXFFGJh4lkg8pbE1RkMpSvLi+t0uTJEmSJGmz2HNQkiRJkt5D64JGGirKyCRiEATkoxFaSpJkoxHSRYW8+YvXe7tESZIkSZI2meGgJEmSJL2H5Xe+TTax3qArQUBzaZJsQYJnnkj1TmGSJEmSJL0PhoOSJEmS9B5awmiP7SXpFDstWEzYVyepkCRJkiRtlwwHJUmSJOk95PIQzWS7NoYhS2oqGT9/IZX1DZu0n0XNsMefYgy5McH5M3sOHCVJkiRJmygIts5XHxTb+CqSJEmStON7+sQnqZvbCkDN+BL2+cNkAEqyGfqvaKSlsIBMIkYknyeayVNfUcbf9t2LCe8u3Oi+FzbDXjfHIBtCJM+Nz4f86Y0YC76Y3ei2kiRJkiRtTfYclCRJktTnPXDYo2RfrWOnxSsYu3gF7S/V8vCRTwAQ5gPS8RixXJ7CtjQFqSyxfJ6y5laIxXhryICN7n+vP8UgFoXSBBTHoTRBKgVps0FJkiRJ0jZmOChJkiSpz0suaWLE0lUk01kK01lGLVlFZH7HcKHRSNjzRqubm4uKNn6AfADxdW6/ggCSMf7wUn4LK5ckSZIkafM4rKgkSZKkPq+8tY0sEdqIkycgQY6K5nbCMKSlvJDyljbqS4qoSLVQkmmjLllCc1lHKJhMZTZ+gEgP81hEAurSW/lEJEmSJEnaCMNBSZIkSX1eezxBLogSrO4N2EaUbDbgx2e8w/D6ZvahndZYgkXlVRRlUuy6dAHxBQEvDh9F5bL6jR8gn4cw0nWy+1yeMJvHAV0kSZIkSduSd6GSJEmS+rza4pLOYHCNSCpk4mvvkKhL88rwoSytqCQVT7CqqJSZIyew06IFVC9fSSyV2/gBYqtvvcLVB8mHHb0Jw03YVpIkSZKkrchwUJIkSVKfF2/vIaQLApYO609rdSlh0PXWKR+JsLSskuqmZur6l2z8AJGgo9fgmp6Dq18HQXQrVC9JkiRJ0qYzHJQkSZLU58UzOdbrOEg6EYUgIB+NEgbd5wwMgYaSQooymzDn4AaEUW/JJEmSJEnblnMOSpIkSerzmioKmT++HwMWNZBIZamvKiKWynYsDAIK2jOkk/HO9YtT7ZStTLPzqhW8Pqrfxg+QC3t8NDMXrh9JSpIkSZL0wTIclCRJktTnLRteQS4R490Jyc628tpmJj4xl/qaIiJkSKcyJLM5kpkMBeksjckSSlpTjFq8cuMHSGchvt4Qovk8CQdzkSRJkiRtY4aDkiRJkvq8fKSHYUODgIJUlgGLGlk5oIAEEM3maI3FaSoooG50OSXtGXZ9c+7GDxB23z9AIhICPS+TJEmSJOmD4GOqkiRJkvq8ZHv3eQMLm1IsGFxJJhaltC4NYUgm1tH7L5rLU7N0FSsripnXv/8mHKCH5zIjEdqyW1q5JEmSJPVRwVb66oPsOShJkiSpzxu1YDnthQkWDKmBENqCGPfuvwsEAYl0lkMf+TfJINVlmwAobGmnsaR44wfYwGOZIc45KEmSJEnatuw5KEmSJKnPq6pvZvycJRz62MuMeXsx/x49GIKOR0jTiRgP7f8RIrm1QV4+CAgDyMZjJFLdex1201MGGIZEvCOTJEmSJG1j9hyUJEmS1OcEby+hYv9LCfNRIEe+6IDOZcuqy+m/soGd315AcVuKpTUVvDR+BGE2QnthlLbiAsJoBMKQRHuatvgmHDAMO76CdcasCTv/T5IkSZKkbcZwUJIkSVKfUzrlv0lTSkgUCFlZkiSRzVGYzjJwRT1lDU1E8x3B3YgltSQyWZqrCkgVrpMEru49mC4q2IQjBl2DwdVN0TCHt2WSJEmSpG3Ju1BJkiRJfUrsNw+QpWj1qzwAJflW5verIJHJkonQGQyuMbC2nqbywh73F8nnN3rMIJcnjHUfQzQS635L1pqBaAAF3q1JkiRJ0nsINr6KeuTtpiRJkqQ+JX7lgwSEhOvcDu278jUeGDCJomyKpkQhufVG+wwDyEd6vvEc0Niw0WOGuRDyIay7j0y4OliMArCsKc++v8pTmMuTJyAfhze/Fe/W4VCSJEmSpC3R/dFVSZIkSdqBBanU6uFE12kLIZGENyaMYMmQfgTrzQXYWlRAzaqWbvuKZbJ8ZMnCjR80GoH2PGTykAshlYN8SGu49pZsyowsA1NZKrJ5qrI5qtty7PKL9Ps7SUmSJEmSNsCeg5IkSZL6lsia4C8kQoY8UZ4fshv/HjOqc5XWnYYyfMFSmpJJ2oqTJNoypFIhhWGKWDpLJAwZ2LySOTVDqCsppN9GDhmEecLC2Np5B6NRyOcJ1nleszrTfXjSZMvGhyyVJEmSJGlzGA5KkiRJ6lvyOWK0EKeBCHlCYN6AQ7uuEo3QWFlKXXkpAO2FORJhlpNfur/LeuNWLeKWXT/OeKA5DTvfkqA927HswIE5bj8yB0AQBITrjw8aBMTCHGtuywK6zpgR0DH3oCRJkiRJW5PDim7HjjrqKM4888zeLuND56KLLmLSpEl95riSJEnaPEEQI0E9ETp65QVASbax23q56NrbpXwsyqiViwEIgRwR8gRUtzYS0JEGjrkhRsniFkYsbmTo0iZmv57hwBtChv88Tz7W861Xbt26Vu97jXC915IkSZIkbQ2Gg5IkSZL6mKDbnIKT580ikl87hGckn6epMNn5urK+kUwYZTn9aKSCdkpopYQ2Cvnk7BdZ8VYzNSvbKM/miAEF+ZABrWnmNAS01xRvsJKbXumY+7C5PSQTDbr1HMzEoj1uJ0mSJEl9XrCVvvoghxWVJEmStGPK54n97inC6mJyn5m4tj1ME64XEA5rWERzQY5cUEAilyMXj1OUThPJ5jjy6WcpSnX0DiwgS6yzx2FAnjjF+RTnzFhGUb8hNBXFiYRQ1Jbp6JGYyVPXnOkYH7RwvWczQ0gsT7HfJTC3JMnQHqYXzMUCRv4y5N1z++gdqyRJkiRpqzMclCRJkrTDifz1RcpOv548MSAkctbvqH/1J1BT0jH/H2GXB0TzwEFznuaOiZ+gIVlN/5ZWRq6ax8eff41IJtG5XrTLQKAdYuT5+567EIQQRjr2mkhnGby0mVwkgEgEcnnIhxBZe9TKhnYKVgeCZW2ZHs+jLRmnsC3Lnr+Cf/2nvQglSZIkSVvOYUW3E/fddx8nn3wyU6dOZfr06Tz66KNdlodhyB133MHnP/95DjjgAKZMmcKxxx7LjTfeSBh2PBE9Y8YMJk2axNy5c7tsm8/nOeKII/jOd74DQGNjIxdddBGf+tSn2G+//TjmmGOYMWMGqVRqs+u+6KKL+PSnP81LL73E5z73OaZOncqxxx7LHXfc0W3dWbNmce6553LIIYew7777csQRR3DppZfS1NQEwFNPPcWkSZO4/fbbu237ve99j8MPP5xcrvsfawCWLFnChRdeyKGHHsqUKVM46aSTuOuuuza7hjVee+01vvKVr3DAAQdwxBFHcMMNN3S+z5IkSep9xaffQJoSshSRpYgMxZTt/H0AcvlIt5FjIsChbz3NT/7vShLZFLkAPjbnRYoyXUO7tmiC9S2LVlJT19IZDAKkEzFWlRfQlIx3NESDLsEgQFvh2mc1qzM5Euv8PhkC9YVxmmIRSlMZ2hv9XVOSJEmStHXYc3A7cM8993DxxRez++67c84557BgwQK+973vEQQBgwYNAuDXv/41v/vd7zjyyCP5zGc+Q0tLC3//+9+ZMWMGxcXFHH/88UybNo0bb7yR+++/n7POOqtz/88//zwrVqxg2rRpAHz3u9/ljTfe4KSTTqKmpoaXXnqJG2+8kYaGBi644ILNrr+hoYFzzjmHqVOncuSRR/Lwww9z2WWX0dTUxGmnnQbA008/zbnnnssee+zBWWedRSQS4emnn+auu+6iqamJyy67jH322Yeqqiruv/9+jj/++M79t7W18fjjj/PpT3+aaLT709SLFi3i1FNPJZ1OM336dKqrq3n44Ye59NJLmT9/Puedd94m1wAwZ84czjzzTMrKyvjSl75EJpPh5ptvJpPp+Wnv3hQ4+pR2cGt+xv1Zl94fryHtyHIUsfZZyICQKHmSBAEEsWCD00pUtTYw7eWZjFlWy06177KSobSvXraitJS5A0dxwJxXSeQ7hhltoJiGXBlf/tvz/PgLB3TZV0NJAaRWh3rR7kdsT8bJBxAJoTUZo66ykEwsSnFLmqpVrdQFEWjNsbggQVk+77WqHY6fQ9KW8RqStozXkKS+zHDwQy6Xy3HVVVex8847c+211xKLdXzLJkyYwMUXXwxANpvl1ltv5ROf+AQXXXRR57af/vSn+cQnPsGTTz7J8ccfz9ixYxk7diwPPvhgl3Dw3nvvpaSkhKlTp1JXV8ezzz7Leeedx+c+97nO/YRhyKJFi97XOTQ1NXHSSSfxzW9+E4DjjjuOL3/5y1x33XUce+yxlJWVccsttzBgwAB+9atfEY/HO9c77bTTeOqppwCIRqMcdthh3H777dTW1lJTUwPAo48+Snt7e2e4ub4ZM2bQ0NDATTfdxIQJEwCYPn063/zmN7n55ps58sgjGTNmzCbVAHDNNdcQBAHXX389AwcOBODQQw/l5JNPfl/vzwelsrKIWMyhp9Q3VFeX9nYJ0nbNa0g7opYe4r88MWpqSmnJd8w2uKG/A41fsJg3C8cxIlhGcVhHigLSFLKkopxVRSU8Wz6OwatWkSVGOwUA7P7O8g0XE4aQzkNivd/N8iFtsSiJfJ6l/Us6/zLVXFpANhaB5o6Hz9LRCO1hSE2N16p2TH4OSVvGa0jaMl5Dkvoiw8EPuddff526ujrOPPPMzmAQ4JOf/CRXXHEFALFYjPvuu49sNttl2/r6eoqLi2lra+tsmzZtGjNmzODtt99m7NixZLNZHnroIQ466CASiQQlJSUUFRVxxx13MHjwYKZMmUJhYSE//OEPt+g8Tj311M5/R6NRTjzxRM4//3yeffZZDj30UK644gqampo6Q7l1629tbe1S/6233sqDDz7ICSecAHQMuTpkyBB23XXXbsfN5XLMnDmTyZMndwaDAJFIhC9+8Ys8/vjjPPbYY4wZM2aTasjn8zz99NNMnTq1MxgEGDlyJJMnT+axxx7bovdpa1q1qtUnn7TDC4KOX+JXrmzCkX2lzec1pB1ZYQ9tAXlqa5soCvPkifY4f2AmiDG/cBhhNODd0mHs0fgq5Szlwf5TaUx0BIFthQmaVxV32e7dfuXd5hQkEkAuB5lcR0BYHHZ9ND0PjYUJkvl8t0fW2wvj0JrtGF8UaIsE1NZ2Hepe2t75OSRtGa8hact4DWlb8SE/fRgZDn7ILVmyBIChQ4d2aY9GowwbNqzzdTwe54knnuDRRx9l3rx5LFiwgMbGRqAj0Fpj2rRpXH311Tz44IOMHTuWZ555hoaGhs5ed4lEgu9///tccsklfOc73yGRSLDXXntx8MEH86lPfYqCgoLNPofy8nKqq6u7tA0fPhyAxYsXd57PokWL+M1vfsPcuXNZuHAhy5d3f/p6t912Y+jQoZ3hYHNzM0899VRnL8f11dfX09rayogRI7otGzVqFLD2Pd6UGhoaGmhtbe32/YCOgPDDFA4C/mKjPiMM/XmXtoTXkHZEmSPGEf/Hm6ztHxjSesVnOn/eI+S7rN8cLaYxVsa/y3ahLVoEQGOiuGN40iBHezxBNJ0nyIc0VBVSVt9OYWvHw3ntiRi//cTE7kUEARRGoSDoCPnWf3IrCq3xKEXtPcybnQ87g0GASMTrVDsuP4ekLeM1JG0ZryFJfVFk46vow6C9vb1bW7j6UysMQ775zW/yne98h8WLF7P77rtz3nnncddddzFgwIAu2wwcOJA99tiD+++/H4D777+f6upqJk2a1LnOtGnT+L//+z8uvPBCpk6dyssvv8xPfvITTjvtNNLp9GbXvm6PxzVyuY4/gEQiHT+Cf/jDH/jCF77ArFmzGD58OJ///Oe58cYbOeKII7pte/jhh/PCCy9QW1vLww8/TCaT4fDDD+/x2OF7fLKvCU3X9BTclBqC1X/Qea/vhyRJknpf5vdfoP1vZ5AfVEJ+bBWtL5xPeMq+AASRCAFdf3d7o2Q8j9YcyMpETWdbRboZgEgYpaA9RbItS1l9CwXtaepqkqQSEWZPHMGc0WVko2x4nNLG95ibOgjIZfNE0+sFhG1dRwWZdcYmnbYkSZIkSRtlz8EPuTU91BYsWNClPQxDFi9ezOjRo/nXv/7F448/zumnn87ZZ5/duU42m6WhoYEhQ4Z02XbatGlcdtllvPvuuzz++OMcccQRRKMd85+0trby5ptvMnr0aI455hiOOeYYMpkMv/zlL/nTn/7E008/zQEHHLBZ51BXV0draytFRUWdbWvOZ/jw4aRSKa655homTZrEjBkzuoSJv/71r7vtb9q0aVx//fU8/vjjzJw5k5122okxY8b0eOzKykoKCwuZN29et2Vr2gYMGLDJNZSXl1NcXNzt+wGwcOHCjb0VkiRJ2oby+4yk/cXvdmsPwwh5YkRYG8Dt3PQKb5TtRJDvSPgq0k2MbeqYczsAhjXV0ZCuIAzSEK7NAdtKEuy9ZCk/XvQ6p4zcv2tAGIbQvPrhutZsx5yD6/YezIUQgze+E+PqWSkumR0jRwCZfMcXIckovHMuRCM+1ylJkiRJ2jq8w/yQGz9+PIMHD+aOO+7o0lvt3nvvpb6+HugY6hLWDpO5xl/+8hfa29s7e+mtceihhxKLxbjmmmtoaGjo0utuzpw5nH766dx9992dbfF4nPHjxwNre/ptjjAMue222zpfZ7NZbrnlFkpKSthnn31IpVK0t7czfPjwLqHcG2+8wfPPP9+5zRqjRo1i/PjxPPLII8yaNWuDvQahY6jQKVOm8PTTT/P66693qen3v/89QRCw//77b3INQRBw0EEH8dRTTzFnzpzO9RYvXszMmTM3+72RJEnSthcEOUIShKtvh0ICXqrajVWVpTSUFzG++V0OWPESibDjd9C5NYN4ZuJOpJIxgnWCwZbiBPFYjrErlvGxK/agpKG965hUYUhRJkOUEPIhkbYM8XSOaDZHvD0LEfj6LikAvvLRgHhTpiNEzKwZ8jQgkgmJRpxIWpIkSZK09dhz8EMuCAK+/e1v861vfYvTTjuNo48+muXLl3PbbbdRXl4OwO67705xcTG/+MUvWLJkCWVlZcyePZv777+fgoICWltbu+yzoqKCyZMnc//99zNkyBB22223zmW77rore+65J7/61a9YunQpO+20E8uWLePWW29l5MiR7Lvvvu/rPK677jqWLFnC6NGjeeCBB3jppZe48MILSSaTJJNJdt11V/76179SXFzMiBEjmDNnDnfffXdnGNna2kpZWVnn/qZNm8aVV15JEATvGQ4CnHPOOcyePZuzzjqL6dOnU1NT0xksnnLKKYwePbrz3DelhrPPPpsnnniCM888k5NPPploNMqtt95KUVHR+xp2VZIkSdtWQJ6AGCFFhGSACP+u3rljiM94lAc+Mok5LcMYvGoF86oH8K8R40lFY4S5ODWLmihpaKelLMnCMTXU1DXQHEsysDTOpQfnuPSfzaQTMaK5PBFCXvlWjHQ2ZPj1cYbVNhPLrQ0P68qTRNcJE3M9ZYB5h66XJEmSpB75HOX7Zji4HfjYxz7G//zP/3DNNdcwY8YM+vfvz4UXXsjtt98OQHV1NVdeeSVXXXUVv/vd74jH44wYMYKf/OQn/Pvf/+bPf/4zK1eupLq6unOf06ZN44knnuATn/hEl2MFQcDPfvYzfvvb3/L4449z1113UVpaysEHH8zZZ5/dOT/f5poxYwb/7//9P/72t78xatQoLr/8cg466KDO5ZdddhlXXHEFf/3rX8lkMgwcOJAvfOELjBo1ivPPP59Zs2ZxyCGHdK5/+OGHc9VVV7HrrrsyaNCg9zz20KFDufHGG/nVr37FnXfeSXt7O6NGjeLCCy/kmGOO2ewaBg4cyPXXX8+VV17JTTfdRCKR4NOf/jQAN9xww/t6fyRJkrTthEEIpImwdmSOfGLtXWU6keC+EXvRWJAgH4mQyOYYsqqexooSmsuKqGpsojVZQCoZJ5ot5JWhwxgInLR7lJN2hycXwNCygOHlHQ+ZJWIBJalMl2AQoKKxnUQQAB1D/Bfm8mTWG6kjmct3LpckSZIkaWsIwjD0UdQ+6L777uP73/8+t99+e7fhSLemiy66iL/97W/Mnj17q+63traWT37yk5x//vkcd9xxW3XfO4oVK5p6uwTpAxcEUFNTSm1tE36aSZvPa0h9VdHYb1DUuLLLQ6a/3O8s0kGCRHuahuJCXho9vMv8gMXtKT72yltMfeV14vkcITB34AAWVlZRmGnj0CePeM9j7nQl1DS0d2kLgRM+EeHrkxMA7HJJitZ4jJZohAAoy+aI5HO88f2CrXPi0oeMn0PSlvEakraM15C2lX79Snu7hB1WcElqq+wn/EHfu+dyzsE+KAxD7rzzTnbdddcPNBj8IN15553E4/FuPR8lSZKkjYkGuW6jz0SCkFRhkqbKMmqrK7oEgwAtBQkm//sN4vmO+bwDYMzSZdTU1VPV3LzRY7YUxcmvd9Dm4gSZ/NpbsguOjFGQyzG0LcWgthTkQx49J/E+zlCSJEmSpA1zWNE+JJvNcsEFF7B06VJeeeUVfvrTn272PnK5HKtWrdqkdUtKSjZ7/xszY8YM5syZw8yZMzn++OO7zEMoSZIkbYr2T+xFwe0PdQkI+zcvp6GwY07vRDbXbZthi2pJhtlu7QW5HCXpjT+tmo1FWNK/lMqGNmLZPK2FceoqCglZOz/4yROjHL9blJ8/kadfccCXJjmBhiRJkiRp6zMc7ENisRgLFixg0aJFnHHGGRx88MGbvY9ly5Zx9NFHb9K6P/zhDzd7/xvT2trKrFmzOPDAA/nqV7+61fcvSZKkHV/m6i8QrhcO1hYVk6djaJXytnbi2SyZ2OrbpTCksqGZNFESdA0Oa6sqWBaUMW5jB82GtCdjLEmuM6RQPmT9LozxKHz3QAd4kSRJkiR9cAwH+5hbbrlli7avrq7m6quv3qR1x4wZw1FHHcVFF120Rcdc1/nnn8/555+/1fYnSZKkvqnpW9Mp+dn/EiVHHmiIVDOnXzVl7SnS0ejaYBAgCHhnxACWP1/OoNwqonRMStMYJHl3SD+KWjZhnotMDuKRrsOV5vIE+TWRpCRJkiRJ24bhoDZLQUEB++67b2+XIUmSJG2R9Pmfou78TxEsWkXYv4zYsf/iwKdepaw+zb8nDGblR4q7rN9UUgQxqM2VESVPSEA8zNN/ZRPNhRufvD4SDcivN48hsQhhdGuelSRJkiRJG2c4KEmSJKnPCodUAlC9rIlh8xoAKGlPd1svkc1SkkqvMwpoR+/BkpY2ViYTGz1ONAjI99BemO2pVZIkSZKkD47j10iSJEnq86pXNHf+e/DSeipaWjtfB/k8w2rryEWDbtvNH1BN1crWbu3ri2WyHXMMrhF2/DuX8JZMkiRJkrRt2XNQkiRJUp+XyOQ6/129opmd5y2ltqqUdCxKSSrFpH+9TSy3TrhHSJwMdVWlRDJh9x2uLxdCZJ1wMQgIcnmi2RzelkmSJEmStiUfU5UkSZLU5xWQYc1QoZF8SNWCBuoKClhRUkSirZUBtY3rbREQIWSneYtIJzc+cWA2GiHIh5Q2p6isbyORyhJGI+TDTQgWJUmSJEndBcHW+eqDDAclSZIk9XnZZIRiUkTIk40HPLHvOGory2hJJnlh9CjykQiZaITaimKaigo6txs9fwnDFy3b6P7DfMiQZU30X9lKVUM7w5Y2UdrQDoG9BiVJkiRJ25Z3opIkSZL6vLqiIiINAAGxTMhn/u957j5iT+qqSshHIjw3YRhhFMJIx/OVpQ3tFDdmKE03U9LcttH9J8OQgnSuS1tVQzthZOO9DiVJkiRJ2prsOShJkiSpzyupTwNrh5NJZHLs8cqCjhdhSGtRojMYBGgqTxLJ5giao5De+DA0Xecr7BANQyL0zSFsJEmSJEm9x3BQkiRJUp+Xi3S/NSpsSwMQCUOSmWy35elkjAAoau6+rNu6sQjrx4NtyRjtobdkkiRJkqRtyztRSZIkSX3ewuHV3dqWDSynvKGFca+/Q1NhQbflhc0ZoCM83JhUELC8uohsNCAEWpMxllcVEeQ2HixKkiRJknoQbKWvPshwUJIkSVKft3xoBYtGVpGJR8jEoywcVUVbdZKf/GYonzo44N2B/SjIdvQkDPIh1UubKWrtCAeL8xufczAXi9JcUsC8oRXMHV7BkgGl5GIRCPvonagkSZIkqdfEersASZIkSeptsXyehWNqWDimpksbQGM6xvjFSxjesJzRK1bQnonTkC8jJKCSRpoLuvcq7Ca+znOZQdD530RhdGuehiRJkiRJG2U4KEmSJKnPq2htobGouEtbWVsrQRCQb8lCIsJHli6mIJuljDb60whAHpg9ZE9Gb2T/kXyO/PoDt4QhJ0/YeucgSZIkSdKmcFhRSZIkSX3ecXd8lMJMimwkQjYSoTCbYvrtewNQVBlhYVU/Yrlct+0WFlVRsWzjw4r+5uMZyHedmzCaydG/xGFFJUmSJEnblj0HJUmSJPV5sXiEz92xN/lcCAFEImtDu1gI8WyWtwcOZvySRZ3t2SDC/NJ+5DMbf+by0zsFzGtq57+fixMGASXRPC+dmv1AzkWSJEmSpPdiOChJkiRJq0Wi3XvytaYjDKpbxZPjdiETjTF85Qqak4U8N2onovVpCtrSm7Tv8/aC8/bKbO2SJUmSJEnaLIaDkiRJkvQeGoMoLYWFhJEIT43fhafWWVaZqSeeMvCTJEmSJG0/nHNQkiRJkt7D+E/0J5bvPt8gQGVLEwWlPnMpSZIkSdp+GA5KkiRJ0nvov9cABtSt6tY+YFUdZS0tTPnHAb1QlSRJkiRJ74+PuEqSJEnSRoz5yngy17xNMpshF4kwZtlikuk0zw0ey97JaG+XJ0mSJEnSJrPnoCRJkiRtxPjPDmFVQTG1ZeUUpVO8NWgoD+yyF0feNLm3S5MkSZKkvinYSl99kOGgJEmSJG2CU/86iZIxJcytGsCq4hKqDpvPkD2re7ssSZIkSZI2i8OKSpIkSdImOuZnHwEgm83yz3++27vFSJIkSZL0PthzUJIkSZIkSZIkSeojDAclSZIkSZIkSZKkPsJwUJIkSZIkSZIkSeojnHNQkiRJkiRJkiRJ25mgtwvYbtlzUJIkSZIkSZIkSeojDAclSZIkSZIkSZKkPsJwUJIkSZIkSZIkSeojDAclSZIkSZIkSZKkPiLW2wVIkiRJkiRJkiRJmyXo7QK2X/YclCRJkiRJkiRJkvoIw0FJkiRJkiRJkiSpjzAclCRJkiRJkiRJkvoIw0FJkiRJkiRJkiSpjzAclCRJkiRJkiRJkvoIw0FJkiRJkiRJkiSpjzAclCRJkiRJkiRJkvoIw0FJkiRJkiRJkiSpjzAclCRJkiRJkiRJkvoIw0FJkiRJkiRJkiSpjzAclCRJkiRJkiRJkvqIWG8XIEmSJEmSJEmSJG2WoLcL2H7Zc1CSJEmSJEmSJEnqIwwHJUmSJEmSJEmSpD7CcFCSJEmSJEmSJEnqIwwHJUmSJEmSJEmSpD7CcFCSJEmSJEmSJEnqIwwHJUmSJEmSJEmSpD7CcFCSJEmSJEmSJEnqIwwHJUmSJEmSJEmSpD7CcFCSJEmSJEmSJEnqIwwHJUmSJEmSJEmSpD4i1tsFSJIkSZIkSZIkSZsl6O0Ctl/2HJQkSZIkSZIkSZL6CMNBSZIkSZIkSZIkqY8wHJQkSZIkSZIkSZL6CMNBSZIkSZIkSZIkqY8wHJQkSZIkSZIkSZL6CMNBSZIkSZIkSZIkqY8wHJQkSZIkSZIkSZL6CMNBSZIkSZIkSZIkqY8wHJQkSZIkSZIkSZL6CMNBSZIkSZIkSZIkqY+I9XYBkiRJkiRJkiRJ0mYJgt6uYLtlz0FJkiRJkiRJkiSpjzAclCRJkiRJkiRJkvoIw0FJkiRJkiRJkiSpjzAclCRJkiRJkiRJkvqIWG8XIEmSJEmSJEmSJG2WoLcL2H7Zc1CSJEmSJEmSJElax8EHH8xVV13V22V8IAwHJUmSJEmSJEmSpD7CcFCSJEmSJEmSJEnqIwwHJUmSJEmSJEmSpM3wl7/8haOPPprdd9+dgw8+mF/96lfkcjny+Tz77bcfN9xwQ+e6v//97xk/fjwvv/xyZ9s555zDBRdc0BulE+uVo0r6wAVOxqo+YM3PuT/v0vvjNSS9f0EA8Xi8899eR9Lm83NI2jJeQ9KW8RqStCVuvPFGfv7zn/Pd736XqVOn8uKLL/KjH/2IVatWccEFF3DggQcyc+ZMTjvtNACefPJJgiDgmWeeYbfddiOTyTBz5kx+9rOf9Ur9hoPSDqqmprS3S5C2mepqf96lLeE1JL0///Ef/wHAgAGVvVyJtH3zc0jaMl5D0pbxGpK2X+G3eifiCsOQ3/72t/zHf/wHp5xyCgAjR46kvr6eyy+/nHPPPZeDDz6Y888/n3Q6TRAEPPvssxxyyCE888wznH766cyePZt8Ps+UKVN65RwcVlSStN1qbm7mwAMPpLm5ubdLkbZLXkPSlvEakraM15C0ZbyGpC3jNSTp/aqrq6O2tpa99967S/s+++xDJpNh7ty5TJ06lVwux3PPPccLL7xAcXExJ5xwArNnzyabzfLII48wdepUkslkr5yDPQclSdu1lpaW3i5B2q55DUlbxmtI2jJeQ9KW8RqStozXkKT3IwzDHtvz+TwAsViM4uJi9t13X2bOnEk8Hmffffdl0qRJZDIZXn75ZR555BHOOuusbVl2F/YclCRJkiRJkiRJkjZBTU0NNTU1PPfcc13aZ8+eTTweZ/jw4QAcdNBBzJw5k2eeeYb99tuPoqIiJk6cyK233sqCBQv4+Mc/3gvVd7DnoCRJkiRJkiRJkrSeefPm8dhjj3VpSyaTfOlLX+KKK65g2LBhTJ06lZdeeokZM2ZwwgknUFraMZfpwQcfzCWXXEI0GuXyyy8HYPLkyVx99dXstddeVFVVbfPzWcNwUJK03UokEpxxxhkkEoneLkXaLnkNSVvGa0jaMl5D0pbxGpK2jNeQpE1xzz33cM8993RpGzJkCA899BCJRILf//73/OQnP2HgwIGcccYZfOlLX+pcb9CgQYwfP57m5maGDBkCwJQpU7jqqqs45JBDtul5rC8INzQ4qiRJkiRJkiRJkqQdinMOSpIkSZIkSZIkSX2E4aAkSZIkSZIkSZLURxgOSpIkSZIkSZIkSX1ErLcLkCRpSy1dupRf/vKXPPfcc6TTaXbddVe+9rWvMWbMmN4uTdruXHrppWQyGS666KLeLkX60Mrn8/z2t7/lL3/5C01NTey111585zvf6ZxgXtKmu+GGG3jqqae49tpre7sUabvR0NDA1VdfzRNPPEFLSwtjx47lnHPOYeLEib1dmrRdqKur44orruCpp54ilUqx11578fWvf52RI0f2dmmStM3Yc1CStF1Lp9Ocd9551NbWcsUVV3DddddRWFjIl7/8ZVatWtXb5UnbjXw+z9VXX81dd93V26VIH3rXXXcdt99+OxdccAG/+93vyOfznHPOOWQymd4uTdqu3H777fz617/u7TKk7c73v/99XnrpJS699FJuuukmxo0bx1e+8hXefffd3i5N2i5861vfYsGCBVx55ZXcdNNNFBQU8OUvf5n29vbeLk2SthnDQUnSdu1f//oXc+bM4ZJLLmHnnXdmzJgx/PjHP6a1tZXHHnust8uTtgvvvPMOp59+On/5y18YOHBgb5cjfahlMhn++Mc/cvbZZ7P//vszbtw4/t//+38sW7aMBx98sLfLk7YLK1as4Otf/zq//OUvGT58eG+XI21XFixYwDPPPMN3v/td9txzT0aMGMH5559Pv379+Oc//9nb5Ukfeo2NjQwaNIgf/OAH7LLLLowaNYrTTz+dFStWMGfOnN4uT5K2GcNBSdJ2bcyYMVx55ZX079+/sy0ajQLQ1NTUW2VJ25XZs2czatQobr31VgYPHtzb5Ugfam+88QYtLS189KMf7WwrLS1lwoQJ/Otf/+rFyqTtx2uvvUYsFuNPf/oTu+66a2+XI21XKioq+J//+R923nnnzrYgCAiCgMbGxl6sTNo+lJWVcemllzJ27FgAVq1axS233MKAAQMYPXp0L1cnSduOcw5KkrZrNTU11NTUdGn785//TCqVYvLkyb1UlbR9Of7443u7BGm7sXz5cgAGDBjQpb1fv34sW7asN0qStjsHHHAABxxwQG+XIW2XSktL2X///bu0PfjggyxYsIBvfvObvVSVtH269NJLueuuu0gkEvziF7+gsLCwt0uSpG3GcFCS9KG2ePFijj766A0uf+CBB6ioqOh8/fDDD3PVVVdx8skndz4JKPVlm3sNSXpva+aiSSQSXdoTiYQ9NiRJ29yLL77Ij370Iw466KBuoaGk93bSSSfx2c9+lttuu41vfvObXH/99UyYMKG3y5KkbcJwUJL0oda/f3/uuOOODS4vLS3t/Pcdd9zB5ZdfzhFHHMF55523LcqTPvQ25xqStHEFBQUApNNpkslkZ/v6ryVJ+qA98sgj/OAHP2CPPfbgkksu6e1ypO3OmmFEL7zwQv79739z66238sMf/rCXq5KkbcNwUJL0oRaLxRg5cuRG1/vlL3/JTTfdxCmnnMLXvvY1giD44IuTtgObeg1J2jRrhhOtra1l6NChne0rVqxgp5126q2yJEl9zK233srPf/5zDjnkEH70ox8Rj8d7uyRpu1BfX88zzzzDIYccQizW8afxSCTC6NGjWbFiRS9XJ0nbTqS3C5AkaUutCQa/9rWv8fWvf91gUJL0gRk3bhzFxcXMnj27s62pqYnXX3+dPffcsxcrkyT1FWtGTJk+fTo/+clPDAalzVBbW8sFF1zArFmzOtuy2Syvv/46o0aN6sXKJGnbsuegJGm7Nnv2bG666SZOPPFEpk2bRm1tbeeyoqIiioqKerE6SdKOJpFIMH36dK666ioqKysZPHgwV155JQMGDOCQQw7p7fIkSTu4efPm8bOf/YyDDjqIU089lZUrV3YuSyaTlJSU9GJ10off2LFjmTJlCpdffjk/+MEPKCsr44YbbqCpqYlTTjmlt8uTpG3GcFCStF279957Afjzn//Mn//85y7LzjjjDM4666zeKEuStAM7++yzyeVyXHLJJaRSKfbcc09mzJjROTSVJEkflAcffJBsNsvDDz/Mww8/3GXZkUceyUUXXdQ7hUnbkZ/85CfMmDGD73//+zQ1NbHnnnvy29/+loEDB/Z2aZK0zQRhGIa9XYQkSZIkSZIkSZKkD55zDkqSJEmSJEmSJEl9hOGgJEmSJEmSJEmS1EcYDkqSJEmSJEmSJEl9hOGgJEmSJEmSJEmS1EcYDkqSJEmSJEmSJEl9hOGgJEmSJEmSJEmS1EcYDkqSJEmSJEmSJEl9hOGgJEmSJEmSJEmS1EcYDkqSJEnaatLpNNdeey1HH300EydOZK+99uKzn/0s1157LalUqsu6d955J+PHj+fOO+/c4P7Gjx/P5z73uR6XtbS0MHHiRMaPH8/LL7/c4zprjrH+1+677860adP45S9/2a2uD9L48eP57ne/u82Ot65XX32VqVOn0tzcDMDBBx+8wfd2e7FgwYLeLqHXvd+fqd76WdwW37OVK1fS2tra+fpXv/oVX/3qVz/w40qSJEnbi1hvFyBJkiRpx5DNZvnSl77ECy+8wKc//WlOOOEEcrkcs2fP5he/+AUPPfQQN910E4lEYqsc74EHHqC9vZ3CwkLuuusudttttw2ue8IJJ7D33nt3vm5vb+fZZ5/l6quv5tVXX+U3v/nNVqnpwyqfz/PDH/6QL37xi5SUlADw/e9/n8LCwl6u7P373//9Xy6++GJeeuml3i5Fm2hbfM8effRRvvWtb3HXXXdRVFQE8P/bu/egrqv8j+PPL4gKlBHe2tSwvNAMxcVYVBBSIFi5WaMpYCqWzmKL46ipWE47wTBtXmgjZLMhjXbRLVmSi9BuIrGyWBjKkFmTu16y0rQFMrQUFn9/+Pt+li98ga9AuenrMcMffs7nc877cz7HGfXt+xwWLFhASEgIFRUVPPjggz/a2CIiIiIiPxdKDoqIiIiISJ8oLS2lurqal19+mbCwMOP6/Pnzyc7OZsOGDeTl5REfH98n4xUVFTFu3DhGjBjB7t27SU5O7jTx6O3tzYwZMyyumZOXpaWl1NbW4u3t3Sdx/S8qLCzk5MmTFnMfGhp6HSPqvQMHDvykVZ/Sez/FN6urq+P8+fMW15ydnZk/fz5paWkEBgZiZ6dNlERERETk5qY/EYuIiIiISJ84dOgQAAEBAR3a4uPjcXBwoLa2tk/Gqq+vZ//+/fj6+hIUFERjYyN79+695n4iIiKA/8Z+o8rJySEkJORnXSko0hszZ87k1KlTlJeXX+9QRERERESuOyUHRURERESkTzg7OwPw5ptvdmhzcnLi4MGDrF+/vk/GKikpoaWlBT8/P0JCQjCZTF2eXdgZcwVRS0uL1fZFixYxceLEDu1ffPEF7u7uZGZmAtDc3MyWLVuIiYnBy8sLT09PYmJiyMvL63L8zs59s3a9vLyc2NhYvLy8+OUvf8nSpUs5fvx4t+948OBBjhw50qFSsP2Zg8HBwaSkpLBz507Cw8Px9PRk5syZ1NXVce7cOZYtW4aPjw+BgYGkp6fT2tpqEW9WVhZbtmxhypQp+Pj48Pjjj/PJJ59YjHkt81RRUcFjjz2Gj48PAQEBLF++nC+++AKAefPm8fbbb3c5h219+OGHJCQk4OPjg4+PD/Pnz+fAgQMd5uPZZ5+loKCAyMhI7r//fsLCwsjNze12jnszd3B1i9zY2Fg8PT3x9fUlMTGRTz/9tMM4ubm5Rv+zZs3i4MGDVuPp6VqxxpbYbFnHnX2z4OBgnnnmGXbu3ElISAje3t7Exsby/vvvW/TV2RmZba8nJycbvydDQkIs7h8+fDje3t42fU8RERERkRudkoMiIiIiItInYmJicHBw4IUXXiAqKorf//73fPDBB1y+fBmg0y0/L168SH19vdWfzhQXF9O/f3+CgoIYPnw4Xl5eVFZWcu7cuWuKef/+/QB4eHhYbY+OjqaxsZGqqiqL6yUlJUY7wNq1a8nIyMDPz49169aRlJTExYsXeeaZZ6ioqLimmKzJz89nyZIlODo6smrVKhISEjh06BCzZ8/uNulTUVGBg4OD1YrO9vbs2cNLL73ErFmzSEpK4tixYyxdupSFCxdiZ2dHcnIy48ePZ8uWLRQUFFg8u3PnTrKzs4mNjTUSSHPnzuXYsWPGPbbO0+7du/n1r3/Nt99+y9KlS5k/fz5VVVUkJCRw/vx5EhMT8fX1BWD9+vXMmTOn03cqKytj3rx5nD59miVLlrBkyRJOnz5NQkICZWVlFvfu27ePtLQ0wsPDWbt2LY6OjqSkpNj0DXs6d7m5ufzmN7+hubmZFStWkJCQQF1dHXFxcRZn87388sukpKQwatQo1qxZw5gxY3jiiSc6xNGbtdKerbHZoqtvVlVVRUpKCuHh4Sxbtoz6+noWLVpEdXX1NY0xZ84cHnroIeDqWktMTLRo9/Pzo7q6mh9++OGa+hURERERudHozEEREREREekT48aNIzMzk6effpqjR49y9OhR/vCHP+Dk5ERwcDBJSUncfffdHZ5LTU0lNTXV5nFOnTrFoUOHmDZtmlGtGBYWRm1tLQUFBSxatKjDM+YEpNnZs2fZs2cPf/7zn/Hw8GDy5MlWxwoNDcXR0ZF33nmHoKAg43ppaSleXl64ublx7tw5iouLWbx4MStXrrR4dvr06ezbt48HH3zQ5vdrr6mpibS0NCIiIkhPTzeuz549m8jISDZu3MjmzZs7fb6mpgY3NzcGDhzY7Vhnz56loKAAd3d3ABobG3nttdeYMGECL774InA1Iern50dlZSWPPPKI8eyZM2fIy8szEq2hoaHExMSQmZlJenq6zfPU2trK888/z/jx43nrrbeMuO+//34WLlxIUVERc+fOpaioiA8//LDDWZJttbS0kJKSwvDhw/nLX/7CLbfcAkBsbCxRUVE899xzBAUF4eDgAMDp06fZtWsX9957LwAPPfQQgYGBFBYWdvsNezJ3DQ0NbNiwAU9PT3Jzc40E+sMPP0xUVBQpKSnk5eVRX1/Pq6++SmhoKJmZmZhMJubOncvIkSONSjno/Vppy9bYbBUQENDpN/vqq6/YvHmzUd06Y8YMwsPD2bRpk9VK5M74+Pjg7u7Ou+++S2hoKCNHjrRod3d3p7m5mdraWiZNmmRzvyIiIiIiNxolB0VEREREpM9MnTqV8vJyysrKeO+996iqqjKSQu+++y7Z2dn4+flZPPPEE08wZcoUq/0tXLiww7Xi4mIAo0IIriYH169fz9tvv201OWgtAWkymZgyZQppaWmYTCar4zs7OxMSEkJZWRnNzc04ODhw/Phxjhw5wrp16wAYOnQoNTU1xhalAFeuXDG2Ir1w4YLVvm31j3/8g6amJkJDQy0SnPb29kyaNImKigpaWlro18/6X+9OnTplJLu6c9dddxnJLcBI5radaycnJwYPHtyhSjMgIMCiAnPMmDEEBgby3nvv0draavM8HT58mHPnzpGYmGiR0PT392fnzp3cc889Nr0LwJEjRzhz5gxPPfWUkRgEGDRoEI899hibNm3i8OHD+Pj4GO/bdq6GDh3KkCFD+Oabb7odqydzt3//fr7//nsWLlxoUVk7cuRIYmJiePPNNzl79iw1NTVcvnyZ2bNnW6zVefPmWSQHe7tW2rI1tmHDhnXbV3fuuecei21vXV1dmTFjBn/605/497//zeDBg3s9BmAkC83b04qIiIiI3KyUHBQRERERkT41YMAAIiIiiIiIAODjjz9m69atFBcX89vf/pbS0lKL+8eOHYu/v7/N/RcVFWEymXB3dzf+kd9kMjF69Gj++c9/UldXh6enp8UzbROQJpMJJycn3NzccHFx6Xa86OhoiouL2b9/P0FBQZSUlGBvb2+8H1zdMrWwsJDKykpOnDjByZMnjWTXlStXbH43az7//HMAli9f3uk99fX1nSZpGhsbLRJjXWmfhLG3tweuJmvaX2//XmPHju3Q3+jRoykvL6exsRFXV1eb5unLL78EwM3NrUN/7b9rd8zrw1rFqjnJ+NVXXxnJwfbvCVe/bfszAq3pydyZ47OW8BwzZowRn3lO7rrrLot7XFxcLMbt7Vppy9bY+iI5aG3tuLm5ceXKFb788ss+Sw6afx80NDT0SX8iIiIiIj9XSg6KiIiIiEivXbx4kS1btuDh4UFYWJhFm4eHB5s2beL8+fP8/e9/p6Ghgdtvv71H4xw5coR//etfAMycOdPqPfn5+R2SSNeagGwrICAAFxcXSktLCQoKorS0lMmTJxsJi0uXLhEfH88nn3zCxIkTmTx5MgkJCfj5+TF16tRrHu8///mPxa/NianU1NQO2ySa3XbbbZ32Z2dnZ1NyC+i0oqyzysq2zFtztmV+Fzs7O5vnyRyrLWN2p6vErLmtbdxtqxqvVW/mzpq28Zn7uHTpUof72n7b3q6VnsTWmfbruCtdrR1zkrUvxjHH3V2fIiIiIiI3OiUHRURERESk1wYMGMBrr72Gj49Ph+Sg2dixY9m3b59NZ991pqioCIDFixfj7e1t0Xb58mVWrVpFSUkJTz/9tMVWiL3h4ODA9OnTeeedd/jss884evSoxdalpaWlHD58mLS0NGbNmmVc//rrr7vt287OjsuXL1tca7+F5YgRI4CrFWjtE5wffPABra2tXb7r4MGDaWxs7DaW3jJXrbV18uRJXFxccHFxYdeuXTbN0y9+8YtO+1u7di0TJkzg0UcftSkm89wdO3asQ9vx48cBuOOOO2zq68fQNr72W7+aY77jjjsYNWoUACdOnLC4r6mpyaIKrrdrpSexgW3ruCudrR17e3sjyWltjJaWFhoaGjpUVHbGPFd9VYkoIiIiIvJz1fP/FikiIiIiIvL/zNtsVldXU1BQ0KG9sbGRv/71r/j7++Po6NijMVpbWykpKcHZ2Zknn3yS0NBQi5+IiAiCg4P59ttv2bNnT29fyUJ0dDQNDQ2kp6fj6OhocY6cOfHWfmvEN954A8A4U8+aIUOG8Omnn1pUuJWUlFjc4+/vz4ABA8jOzqa5udm4/vXXX/Pkk0+ycePGLqvT7rzzTk6fPt39S/bS3r17je0vAT777DMqKyuNZLGt83Tffffh6upKfn6+RTKopqaG/Px8Ll68CPy3yq+rqkgPDw+GDh3Kjh07aGpqMq43NTWxfft2hg4dyn333dfTV+4187fdtm2bxbueOXOGoqIiPD09GTx4MP7+/jg5OZGTk2OxnnJzc63219O10pPYwLZ1DJ1/s48++oja2lrj19988w2FhYVMmjTJqHQcMmQIx48f54cffjDu27t3b4dqSvMY1qpGzYnoO++8s/sJEBERERG5galyUERERERE+kRycjJ1dXWsXr2awsJCAgMDueWWW/j888/Jz8+nubmZZ599tsf9HzhwgDNnzvDoo4/i5ORk9Z7Y2Fj+9re/kZ+fb3EmYG9NmDCBESNGUF5eTmRkJM7Ozkabv78//fr1Y/Xq1cydO5d+/fpRXl5OZWUlDg4Oxpl61kRFRbF161aSkpKYOnUqH3/8MaWlpRbn1Lm6urJixQqef/555syZQ0xMDC0tLWzfvp1Lly6xZs2aLmOfNGkSGRkZnD9/nkGDBvV+MjphMpmIi4tj3rx5NDc3k5OTg6urK0uXLgVsn6f+/fuTnJzMmjVriIuLIyYmhgsXLvDGG28wZswYo2rQPEcZGRnGNqXtOTg4sG7dOpYvX87MmTONisW8vDzOnj1LRkZGr7YS7a3bb7/d+LZxcXFER0dz4cIFduzYQWtrK+vWrQOunpW3atUqnnvuORYsWMD06dM5evQohYWFFsn23q6VnsQGtq1jc3zQ8Zv179+fxYsXs2DBAgYOHMj27dtpbW1l9erVFmOkpqayaNEiYmJiOHnyJG+99ZZR4dh+jOzsbIKCgggJCTHaamtrcXJywsvLy+Z5EBERERG5ESk5KCIiIiIifcJc7fX6669TVlbG5s2b+f777xk2bBhhYWEkJiYybNiwHvdv3lK0s7MG4WoCys3NjaqqKpu29bSVyWQiOjqaV155haioKIu28ePHk5GRQWZmJunp6Tg7OzNu3Di2bdvG9u3bqa6uprm52eq5asuWLaOlpYXdu3dTWVmJl5cXOTk5PPXUUxb3JSQkMHz4cLZt28aLL77IwIED8fDwYMOGDTzwwANdxh4UFMRLL71ETU0N06ZN6/1kdGL69OmMGjWK7OxsWltbCQgIYNWqVcY3v5Z5mjFjBrfeeiuvvPIKmzZtYtCgQUybNo2VK1caieG4uDjef/99srOz+eijj6wmBwF+9atfcdttt5GVlcXmzZvp168fXl5epKWl4evr+6PNh60SEhIYNmwYW7duNSpT/fz8SEpKwt3d3bgvPj6eW2+9lVdffZUXXniB0aNHk5WV1SHh15u10tPYbF3HnX0zb29vIiMjycrK4rvvvsPX15eVK1dabGcaHx9PY2MjeXl5pKamcu+995KZmcnWrVuNalKAyMhI4z8IVFdXWyQHa2pqmDhxYp9tOSwiIiIi8nNlutLVCe0iIiIiIiLys/fwww8zduxYNm7c+KP07+7uziOPPMLvfve7H6V/uXEFBwczYsQI/vjHP/6o45w4cYLw8HCysrIsEoYiIiIiIjcjnTkoIiIiIiJyg3v88cfZs2ePxbl7IjeTXbt2cffddxMcHHy9QxERERERue6UHBQREREREbnBRUZGMnr06B+9Okvkf1FTUxM7duxgxYoVmEym6x2OiIiIiMh1p+SgiIiIiIjIDc7e3p7U1FRycnL47rvvrnc4Ij+p119/nQceeICwsLDrHYqIiIiIyP8EnTkoIiIiIiIiIiIiIiIicpNQ5aCIiIiIiIiIiIiIiIjITULJQREREREREREREREREZGbhJKDIiIiIiIiIiIiIiIiIjcJJQdFREREREREREREREREbhJKDoqIiIiIiIiIiIiIiIjcJJQcFBEREREREREREREREblJKDkoIiIiIiIiIiIiIiIicpNQclBERERERERERERERETkJvF/iAk9l6ZpsjsAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 2000x1500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "val_dataset = catboost.Pool(data=X_test, label=y_test)\n",
        "shap_values = explainer.shap_values(val_dataset)\n",
        "shap.summary_plot(shap_values, X_test, max_display=15, plot_size=(20, 15))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Предсказание для сабмита\n",
        "MONTH_THRESHOLD = 0.701702\n",
        "DAY_THRESHOLD = 0.655656"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Подгрузка моделей\n",
        "month_clf, day_clf = (\n",
        "    catboost.CatBoostClassifier().load_model(\"../models/\" + \"month_predict_clf_v1\"),\n",
        "    catboost.CatBoostClassifier().load_model(\"../models/\" + \"10_day_predict_clf_v1\"),\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Memory usage of dataframe is 8.68 MB\n",
            "Memory usage after optimization is: 4.53 MB\n",
            "Decreased by 47.8%\n"
          ]
        }
      ],
      "source": [
        "# Данные для предсказания\n",
        "data = optimize_df_memory(\n",
        "    pd.read_feather(conf.path_processed_folder + \"data_predict.feather\")\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Для каких вагонов надо предсказать\n",
        "subm_data = pd.read_csv(\"../data/raw/submission/y_test.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {},
      "outputs": [],
      "source": [
        "merged_sub = pd.merge(\n",
        "    subm_data,\n",
        "    data,\n",
        "    how=\"left\",\n",
        "    left_on=[\"wagnum\", \"month\"],\n",
        "    right_on=[\"wagnum\", \"month_x\"],\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Удаление ненужных признако (чисто технических)\n",
        "drop_features = [\n",
        "    \"wagnum\",\n",
        "    \"month\",\n",
        "    \"month_x\",\n",
        "    \"month_y\",\n",
        "    \"month_to_predict\",\n",
        "]\n",
        "\n",
        "X_predict = merged_sub.drop(columns=drop_features)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {},
      "outputs": [],
      "source": [
        "merged_sub[\"target_month\"] = (\n",
        "    month_clf.predict_proba(X_predict)[:, 1] > MONTH_THRESHOLD\n",
        ").astype(int)\n",
        "merged_sub[\"target_day\"] = (\n",
        "    day_clf.predict_proba(X_predict)[:, 1] > DAY_THRESHOLD\n",
        ").astype(int)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Генерация посылки\n",
        "merged_sub[[\"wagnum\", \"month\", \"target_month\", \"target_day\"]].to_csv(\n",
        "    \"../data/final/submission_v4_1000.csv\", index=False\n",
        ")"
      ]
    }
  ],
  "metadata": {
    "anaconda-cloud": {},
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 4
}
